SAP-C02 AWS Certified Solutions Architect - Professional Questions and Answers

B is correct because Amazon FSx for Lustre provides a high-performance, POSIX-compliant file system that can be directly linked with Amazon S3 using data repository integration. This allows the EC2-based processing engine (which requires POSIX file operations) to read/write files through a mounted Lustre file system while automatically importing objects from S3 and exporting results back to S3 using Lustre’s S3 integration policies. This satisfies all requirements: POSIX file access, no need to change the application to use the S3 API, and automated synchronization for both inputs and outputs.

Why the other options are incorrect:

A: DataSync is designed for scheduled or task-based data transfers. It does not provide a mounted POSIX file system interface for the application to read/write “in place,” and the “without an agent” requirement is not a fit for an EC2-hosted POSIX workflow that needs continuous file semantics.

C: Amazon EFS is POSIX and mountable, but “data repository associations to S3” is not an EFS capability in the way described. EFS does not provide native bidirectional automatic sync with S3 using import/export policies like FSx for Lustre’s S3 integration.

D: S3 File Gateway provides a file interface backed by S3, but keeping cache coherent with explicit RefreshCache calls is operationally complex and not truly “automatic synchronization” in the sense required. It also introduces caching behaviors that can complicate immediate availability expectations.

This solution will allow the external auditors to have read-only access to the company ' s AWS account while being compliant with AWS security best practices. By creating an IAM role, which is a secure and flexible way of granting access to AWS resources, and trusting the auditors ' AWS account, the company can ensure that the auditors only have the permissions that are required for their role and nothing more. Assigning a unique external ID to the role ' s trust policy, it will ensure that only the auditors ' AWS account can assume the role.

RDS Proxyoptimizes and pools DB connections. By combining this withPrivateLink + NLB, other accounts can securely access the proxy endpoint without going over the internet or setting up peering for every new account.

RDS Proxy Docs

B and E are correct. A multi-node Amazon EKS cluster provides high availability for the Kubernetes control and workload layer, while Amazon Managed Service for Prometheus and Amazon Managed Grafana reduce monitoring overhead compared with self-managed Prometheus and Grafana. Amazon ECR enhanced scanning is also appropriate because it uses Amazon Inspector to scan container images for operating system and programming language package vulnerabilities. For storage, Amazon FSx for NetApp ONTAP is the correct multiprotocol shared file system because it supports access patterns such as NFS and SMB through a fully managed service. EFS plus FSx for Windows splits storage across two services and does not provide one multiprotocol storage platform. A single-node EKS cluster is not highly available.

https://aws.amazon.com/about-aws/whats-new/2020/11/announcing-amazon-mq-rabbitmq/

D is correct because the requirement explicitly calls for (1) dedicated private connectivity, (2) automated and centrally managed operation, and (3) connectivity between on-premises data centers and multiple AWS Regions.

AWS Direct Connect provides dedicated private connectivity from on-premises environments to AWS. This is the key differentiator versus internet-based connectivity options when performance and consistency are required to meet strict SLAs.

AWS Cloud WAN provides centralized, policy-based management of a global network across Regions and on-premises attachments. Using Direct Connect attachments to Cloud WAN lets the company centrally define routing/segmentation policies and connect multiple data centers to multiple Regions in a consistent, automated way—well aligned to “automated and centrally managed” requirements for a multi-Region payment platform.

Why the other options are incorrect:

A (Global Accelerator) improves performance for internet-facing endpoints using Anycast and edge routing, but it does not provide private, dedicated connectivity between on-premises networks and AWS services. It is not the right tool for private HSM-to-AWS service connectivity.

B (Site-to-Site VPN + Transit Gateway) can be centrally routed with Transit Gateway, but VPN tunnels run over the public internet and are not “dedicated private connectivity.” They can be acceptable for many cases, but they do not best meet “dedicated private connectivity” and strict SLA expectations compared to Direct Connect.

C (CloudHSM) changes the architecture by moving tokenization into AWS-managed HSM infrastructure. The question states the company manages on-premises HSMs and needs private connectivity between those HSMs and AWS payment services, so replacing them with CloudHSM does not satisfy the stated connectivity requirement.

Option A, Deploy the security tool on EC2 instances in a new Auto Scaling group in the existing VPC, allows the company to use its existing security tool while still running it within the AWS environment. This ensures that all packets coming in and out of the VPC are inspected by the security tool in real time. Option D, Provision a Gateway Load Balancer for each Availability Zone to redirect the traffic to the security tool, allows for high availability within an AWS Region. By provisioning a Gateway Load Balancer for each Availability Zone, the traffic is redirected to the security tool in the event of any failures or outages. This ensures that the security tool is always available to inspect the traffic, even in the event of a failure.

https://docs.aws.amazon.com/AmazonS3/latest/userguide/MultiRegionAccessPointRequestRouting.htmlhttps://stackoverflow.com/questions/60947157/aws-s3-replication-without-versioning#:~:text=The%20automated%20Same%20Region%20Replication,is%20replicated%20between%20S3%20buckets.

The correct answer is C. To add an existing standalone AWS account to an organization, the invitation must be initiated from the organization’s management account. The invited account then accepts the invitation. For accounts that are invited into an organization, the OrganizationAccountAccessRole role is not automatically created in the same way it is when an account is created through Organizations. AWS documentation explains that an equivalent administrator role can be created manually in the invited member account. Option A reverses the invitation flow. Option B is unnecessary because AWS Support is not required for the standard account invitation process. Option D is incomplete because a standalone account cannot independently configure itself as a member without accepting an invitation from the management account. (AWS Documentation)

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This option allows the solutions architect to use a DeletionPolicy attribute to specify how AWS CloudFormation handles the deletion of an S3 bucket when the stack is deleted1. By setting the value of Delete, the solutions architect can instruct CloudFormation to delete the bucket and all of itscontents1. This option does not require any major changes to the application’s architecture or any additional resources.

Deletion policies

The best solution is to upload files from the mobile software directly to Amazon S3, use S3 event notifications to create a message in an Amazon Simple Queue Service (Amazon SQS) standard queue, and invoke an AWS Lambda function to perform image processing when a message is available in the queue. This solution will ensure that image processing can scale to handle the load, as Amazon S3 can store any amount of data and handle concurrent uploads, Amazon SQS can buffer the messages and deliver them reliably, and AWS Lambda can run code without provisioning or managing servers and scale automatically based on the demand. This solution will also notify the user when processing is complete by sending a push notification to the mobile app using Amazon Simple Notification Service (Amazon SNS), which is a web service that enables applications to send and receive notifications from the cloud. This solution is more cost-effective than using Amazon MQ, which is a managed message broker service for Apache ActiveMQ that requires a dedicated broker instance, or S3 Batch Operations, which is a feature that allows users to perform bulk actions on S3 objects, such as copying or tagging, but does not support custom code execution. This solution is also more suitable than using Amazon Simple Email Service (Amazon SES), which is a web service that enables applications to send and receive email messages, but does not support push notifications for mobile devices. References: Amazon S3 Documentation, Amazon SQS Documentation, AWS Lambda Documentation, Amazon SNS Documentation

Using AWS Transfer Family to create an FTP server that places the files in Amazon S3 and using S3 event notifications through Amazon Simple Notification Service (Amazon SNS) to invoke an AWS Lambda function will ensure that the archive always receives the files and that the central system is always updated. This solution maximizes scalability and eliminates the need for manual intervention, such as rebooting the EC2 instance.

To offer services to other companies using AWS without traversing the internet, creating a VPC Endpoint Service hosted behind an Application Load Balancer (ALB) and making it available over AWS Direct Connect (DX) is the most suitable solution. This approach ensures that the service traffic remains within the AWS network, adhering to the requirement that connectivity must not traverse the internet. An ALB is capable of handlingHTTP/HTTPS traffic, making it appropriate for web-based services. Utilizing DX for connectivity between the on-premises data center and AWS further secures and optimizes the network path.

AWS Direct Connect Documentation: Explains how to set up DX for private connectivity between AWS and an on-premises network.

Amazon VPC Endpoint Services (AWS PrivateLink) Documentation: Provides details on creating and configuring endpoint services for private, secure access to services hosted in AWS.

AWS Application Load Balancer Documentation: Offers guidance on configuring ALBs to distribute HTTP/HTTPS traffic efficiently.

https://docs.aws.amazon.com/elasticbeanstalk/latest/dg/using-features.CNAMESwap.html

https://aws.amazon.com/blogs/compute/operating-lambda-performance-optimization-part-1/

To restrict IAM actions to only administrator roles across all AWS accounts in an organization, the most operationally efficient solution is to create a Service Control Policy (SCP) that allows IAM actions exclusively for administrator roles and apply this SCP to the root Organizational Unit (OU) of AWS Organizations. This method ensures a centralized governance mechanism that uniformly applies the policy across all accounts, thereby minimizing the need for individual account-level configurations and reducing operational complexity.

AWS Documentation on AWS Organizations and Service Control Policies offers comprehensive information on creating and managing SCPs for organizational-wide policy enforcement. This approach aligns with AWS best practices for managing permissions and ensuring secure and compliant account configurations within an AWS Organization.

The best solution for this problem is to update the visibility timeout for the SQS queue to 3 hours. This is because when the visibility timeout is set to 1 hour, it means that if the EC2 instance doesn ' t process the message within an hour, it will be moved to the dead-letter queue. By increasing the visibility timeout to 3 hours, this should give the EC2 instance enough time to process the message before it gets moved to the dead-letter queue. Additionally, configuring scale-in protection for the EC2 instances during processing will help to ensure that the instances are not terminated while the messages are being processed.

https://aws.amazon.com/blogs/storage/new-enhancements-for-moving-data-between-amazon-fsx-for-lustre-and-amazon-s3/

AWS DataSync is an online data transfer service that is designed to help customers get their data to and from AWS quickly, easily, and securely. Using DataSync, you can copy data from your on-premises NFS or SMB shares directly to Amazon S3, Amazon EFS, or Amazon FSx for Windows File Server. DataSync uses a purpose-built, parallel transfer protocol for speeds up to 10x faster than open source tools. DataSync also has built-in verification of data both in flight and at rest, so you can be confident that your data was transferred successfully. DataSync allows you to apply filters to select which files or folders to transfer, based on file name, size, or modification time. You can also schedule your DataSync tasks to run daily, weekly, or monthly, or on demand. DataSync is integrated with AWS Direct Connect, so you can take advantage of your existing private connection to AWS. DataSync is also a fully managed service, so you do not need to provision, configure, or maintain any infrastructure for data transfer.

Option A is incorrect because the Amazon S3 CopyObject API operation does not support filtering or scheduling, and it would require you to write and maintain custom scripts to automate the backup process.

Option B is incorrect because AWS Backup does not support filtering or transferring data from on-premises sources to Amazon S3. AWS Backup is a fully managed backup service that makes it easy to centralize and automate the backup of data across AWS services.

Option D is incorrect because AWS Snowball Edge is a physical device that is used for offline data transfer when network bandwidth is limited or unavailable. It is not suitable for daily backups or incremental transfers. AWS Snowball Edge also does not support filtering or scheduling.

1: Considering four different replication options for data in Amazon S3

2: Protect your file and backup archives using AWS DataSync and Amazon S3 Glacier

3: AWS DataSync FAQs

https://aws.amazon.com/storagegateway/file/

https://docs.aws.amazon.com/fsx/latest/WindowsGuide/migrate-files-to-fsx-datasync.html

https://docs.aws.amazon.com/systems-manager/latest/userguide/prereqs-operating-systems.html#prereqs-os-windows-server

The best solution is to create a tag policy that contains the allowed project tag values in the organization’s management account and create an SCP that denies the cloudformation:CreateStack API operation unless a project tag is added. A tag policy is a type of policy that can help standardize tags across resources in the organization’s accounts. A tag policy can specify the allowed tag keys, values, and case treatment for compliance. A service control policy (SCP) is a type of policy that can restrict the actions that users and roles can perform in the organization’s accounts. An SCP can deny access to specific API operations unless certain conditions are met, such as having a specific tag. By creating a tag policy in the management account and attaching it to each OU, the organization can enforce consistent tagging across all accounts. By creating an SCP that denies the cloudformation:CreateStack API operation unless a project tag is added, the organization can prevent users from creating new resources without proper tagging. This solution will meet the requirements with the least effort, as it does not involve creating additional resources or modifying existing ones. References: Tag policies - AWS Organizations, Service control policies - AWS Organizations, AWS CloudFormation User Guide

Option A is correct because SageMaker AI Notebook Instances support Git repositories as SageMaker resources, and private repository credentials can be stored in AWS Secrets Manager. This satisfies the requirement with the least operational overhead because the notebook instances can remain isolated from direct internet access while SageMaker manages repository association and authentication. Placing credentials inside a URL is insecure and does not properly solve credential management. NAT gateway options introduce internet egress into the VPC, which conflicts with the isolation requirement and adds operational and security complexity. Network ACLs are not suitable for controlling access to a specific Git URL because they operate at the subnet and IP/port level, not at an application URL level. (AWS Documentation)

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Create Amazon S3 gateway endpoint in the VPC and add a VPC endpoint policy. This VPC endpoint policy will have a statement that allows S3 access only via access points owned by the organization.

https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage_lens.html

The core issue is high latency when serving images during traffic spikes. The current design resizes images dynamically and caches them locally inside ECS containers. With AWS Fargate, containers are ephemeral and scale in and out based on load. Local container caches are not shared across tasks, so cache hit rates drop during scaling events, forcing repeated image resizing. This increases CPU usage, startup latency, and response times under load.

The lowest operational overhead and most effective solution is to offload image delivery entirely from the application layer and use edge caching. Amazon CloudFront is designed to cache static content at edge locations close to end users, dramatically reducing latency and backend load. Amazon S3 is a durable, highly scalable object store that is well suited for serving static assets such as images.

Option A moves image delivery to a CloudFront + S3 architecture. By pre-scaling images and storing them in S3, the application no longer performs on-demand image resizing during requests. CloudFront caches the images at edge locations, so subsequent requests are served directly from the edge with minimal latency. The ALB remains the origin for dynamic content, while image requests are routed to the S3 origin using cache behaviors. This approach is fully managed, highly scalable, and requires minimal changes to the application logic once images are pre-generated.

Option B introduces ElastiCache, which adds operational overhead and complexity. The application must manage cache keys, eviction, and failure scenarios. ElastiCache is better suited for low-latency data access patterns rather than large static objects like images, and it does not provide global edge caching.

Option C adds unnecessary complexity by introducing an Aurora database as part of the caching layer. Databases are not designed to manage cache metadata for static assets at scale, and this design increases operational overhead without improving latency compared to CloudFront.

Option D is not appropriate because API Gateway caching is intended for API responses, not for efficient delivery of large static binary objects such as images. Replacing the ALB with API Gateway also introduces request size, payload, and cost considerations and does not provide global edge caching comparable to CloudFront for image delivery.

Therefore, using Amazon CloudFront with Amazon S3 for pre-scaled images provides the lowest latency, highest scalability, and least operational overhead.

The best answer is C because the workload has millions of small 64 KB uploads every hour. Sending every S3 object event through EventBridge to an ECS task creates high event and invocation overhead. Amazon Data Firehose can buffer many incoming records and deliver larger compressed objects to S3, which reduces the small-object problem and improves cost efficiency. The ECS processing takes an average of 15 minutes, so replacing it with Lambda is risky because Lambda has a maximum runtime of 900 seconds, exactly 15 minutes. For five-year retention, S3 Lifecycle rules can transition data to S3 Glacier Deep Archive for long-term low-cost archival. DynamoDB TTL is appropriate for automatically expiring 15-day results without custom cleanup jobs. (AWS Documentation)

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https://docs.aws.amazon.com/controltower/latest/userguide/controls.html https://docs.aws.amazon.com/controltower/latest/userguide/strongly-recommended-controls.html#ebs-enable-encryption AWS is now transitioning the previous term ' guardrail ' new term ' control ' .

Storing the database credentials in AWS Secrets Manager as a secret that is associated with an AWS Key Management Service (AWS KMS) customer managed key will enable encrypting and managing the credentials securely1. AWS Secrets Manager helps you to securely encrypt, store, and retrieve credentials for your databases and other services2. Attaching a role to each Lambda function to provide access to the secret will enable retrieving the credentials programmatically1. Restricting access to the secret and the customer managed key so that only members of the IT security team’s IAM user group can access them will enable meeting the security requirements1.

The requirement is to continuously scan all AWS resources in this solution for security vulnerabilities with the least operational overhead. The environment includes Amazon EKS worker nodes in a managed node group and an Amazon ECR repository that stores container images used by the EKS cluster.

Amazon Inspector is a managed vulnerability scanning service that integrates directly with multiple AWS resource types. It can automatically discover Amazon EC2 instances (including EKS managed node group instances) and perform continuous vulnerability assessments against them, using software inventory and known CVE databases. Inspector also integrates with Amazon ECR to scan container images for vulnerabilities when images are pushed to the repository and periodically thereafter, providing continuous assessment of image security posture without requiring separate scanners or infrastructure.

By activating Amazon Inspector for the account and region, the service will automatically start tracking supported resources, including EKS-managed EC2 instances and ECR repositories, and will begin scanning them for known vulnerabilities. Findings are provided through the Inspector console and can be integrated with other services such as AWS Security Hub or Amazon EventBridge for further aggregation and automation, but no separate infrastructure deployment or maintenance is required.

Option B, therefore, provides end-to-end vulnerability scanning for both the EKS nodes and the ECR images with minimal operational overhead, because it leverages a fully managed AWS service that automatically discovers and scans supported resources.

Option A is not correct because AWS Security Hub does not itself perform vulnerability scanning. Security Hub aggregates and normalizes security findings from various AWS services, including Amazon Inspector, GuardDuty, and others. It is a security posture management and aggregation service, not a vulnerability scanner.

Option C introduces additional operational overhead by requiring the company to provision, configure, secure, and maintain a separate EC2 instance running a third-party vulnerability scanning tool. While ECR basic scan-on-push can detect some image vulnerabilities, the EC2- based scanner must be managed, updated, and scaled by the customer, which violates the requirement for the least operational overhead when a fully managed alternative exists.

Option D is incorrect because the Amazon CloudWatch agent is used for metrics and log collection from EC2 instances and other environments; it does not perform vulnerability scanning or CVE analysis. Configuring CloudWatch for monitoring and logging does not meet the requirement to continuously scan for security vulnerabilities. ECR basic scans also provide only image-level scanning and do not cover the EKS nodes themselves.

Therefore, enabling Amazon Inspector to scan both the EKS nodes and the ECR repository, as described in option B, is the solution that meets the continuous vulnerability scanning requirement with the least operational overhead.

https://docs.aws.amazon.com/AmazonECR/latest/userguide/ecr-eventbridge.html " Activating an AWS Step Functions state machine " https://docs.aws.amazon.com/step-functions/latest/dg/tutorial-creating-lambda-state-machine.html

The performance symptom is classic Lambda cold-start variability. The API is used sporadically, so Lambda execution environments may be recycled between requests. The first request takes longer because Lambda must initialize an execution environment; subsequent identical requests are faster because the environment is already warm. Provisioned concurrency keeps execution environments initialized and ready to respond, producing more consistent latency. AWS Application Auto Scaling can manage provisioned concurrency with target tracking, which helps align provisioned capacity with usage patterns. DynamoDB on-demand mode already scales automatically for table throughput, and DAX would mainly help read-heavy caching scenarios, not Lambda initialization delay. CloudFront may reduce network latency for cacheable content, but it does not remove Lambda cold starts for dynamic API calls. (AWS Documentation)

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Setting up an AWS Client VPN endpoint and integrating it with Active Directory Domain Services (AD DS) using an AD Connector directory enables secure remote access to internal services deployed in a VPC. Enabling multi-factor authentication (MFA) for AD Connector enhances security by adding an additional layer of authentication. This solution meets the company ' s requirements for secure remote access through a VPN with MFA, ensuring that the security policy is adhered to while providing a seamless experience for the remote engineers.

AWS Documentation on AWS Client VPN and AD Connector provides detailed instructions on setting up a Client VPN endpoint and integrating it with existing Active Directory for authentication. This solution aligns with AWS best practices for secure remote access to AWS resources.

CloudFront origin failover is implemented by using an origin group with a primary origin and a secondary origin. CloudFront can automatically fail over to the secondary origin when the primary origin fails under configured conditions. The default origin connection behavior can take up to 30 seconds because CloudFront uses multiple connection attempts and timeout values. AWS documentation states that reducing the connection timeout and reducing the number of connection attempts can make failover occur faster. Option C correctly adds the replicated S3 bucket as a second origin, creates an origin group, updates the default cache behavior to use that origin group, and sets both connection timeout and connection attempts to 1. Option B misses updating the cache behavior and uses the wrong timeout. Lambda-based promotion is unnecessary operational overhead.

in this solution, two transit VIFs are created - one from the DX-A connection and one from the DX-B connection - into the same Direct Connect gateway for high availability. Both the eu-west- 1 and us-east-1 transit gateways are then associated with this Direct Connect gateway. The transit gateways are then peered with each other to support cross-Region routing. This solution meets the requirements of the company by creating a highly available connection between the on-premises data center and the VPCs in both the eu-west-1 and us-east-1 regions, and by enabling direct traffic routing between VPCs in those regions.

A: Use dynamic IAM policies with {aws:PrincipalTag/userName} to enforceprefix-level access control— i.e., bucket/userA/*, bucket/userB/*.

C: Enable CloudTraildata eventsto capture object-level access andquery them withAthena. This is the AWS-recommended way to audit per-user object access.

Incorrect:

B doesn ' t provide user isolation.

D only capturesmanagement events, not object-level data access.

E is legacy, inefficient, and not structured for per-user auditing.

The company should create a second target group that is referenced by the ALB. The company should deploy the new logic to EC2 instances in this new target group. The company should update the ALB listener rule to use weighted target groups. The company should configure ALB target group stickiness. This solution will meet the requirements because weighted target groups are a feature that enables you to distribute traffic across multiple target groups using a single listener rule. You can specify a weight for each target group, which determines the percentage of requests that are routed to that target group. For example, if you specify two target groups, each with a weight of 10, each target group receives half the requests1. By creating a second target group and deploying the new logic to EC2 instances in this new target group, the company can have two versions of its business logic running in parallel. By updating the ALB listener rule to use weighted target groups,the company can control how much traffic is sent to each version. By configuring ALB target group stickiness, the company can ensure that a customer uses the same version of the business logic during the testing window. Target group stickiness is a feature that enables you to bind a user’s session to a specific target within a target group for the duration of the session2.

The other options are not correct because:

Creating a second ALB and deploying the new logic to a set of EC2 instances in a new Auto Scaling group would not be as cost-effective or simple as using weighted target groups. A second ALB would incur additional charges and require more configuration and management. Updating the Route 53 record to use weighted routing would not ensure that a customer uses the same version of the business logic during the testing window, as DNS caching could affect how requests are routed.

Creating a new launch configuration for the Auto Scaling group and replacing it on the Auto Scaling group would not allow for gradual traffic shifting between versions. A launch configuration is a template that an Auto Scaling group uses to launch EC2 instances. You can specify information such as the AMI ID, instance type, key pair, security groups, and block device mapping for your instances3. However, replacing the launch configuration on an Auto Scaling group would affect all instances in that group, not just 10% of customers.

Creating a second Auto Scaling group and changing the ALB routing algorithm to least outstanding requests (LOR) would not allow for controlled traffic shifting between versions. A second Auto Scaling group would require more configuration and management. The LOR routing algorithm is a feature that enables you to route traffic based on how quickly targets respond to requests. The load balancer selects a target from the target group with the fewest outstanding requests4. However, this algorithm does not take into account customer sessions or weights.

Option A is correct because AWS Compute Optimizer provides right-sizing recommendations for Amazon ECS services on AWS Fargate, including task CPU, task memory, and container-level CPU and memory values. Since the workload is deployed through CloudFormation, the correct operational path is to update the ECS task definition in the CloudFormation template and redeploy the stack. That keeps infrastructure as code as the source of truth. Target tracking policies adjust the number of running tasks, not the CPU and memory size assigned to each Fargate task, so option B does not directly fix over-provisioned task resources. Cost Explorer Reserved Instance reports are not the right tool for Fargate task right-sizing because Fargate does not use EC2 Reserved Instances in the same way.

https://aws.amazon.com/blogs/aws/new-attributes-based-access-control-with-aws-single-sign-on/

However A ' s explanation is incorrect -https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps.html

" SCPs are similar to AWS Identity and Access Management (IAM) permission policies and use almost the same syntax. However, an SCP never grants permissions. "

SCPs alone are not sufficient to granting permissions to the accounts in your organization. No permissions are granted by an SCP. An SCP defines a guardrail, or sets limits, on the actions that the account ' s administrator can delegate to the IAM users and roles in the affected accounts. The administrator must still attach identity-based or resource-based policies to IAM users or roles, or to the resources in your accounts to actually grant permissions. The effective permissions are the logical intersection between what is allowed by the SCP and what is allowed by the IAM and resource-based policies.

B. Add an outbound rule to the EC2 instances ' security group. Specify the DB cluster ' s security group as the destination over the default Aurora port. This allows theinstances to make outbound connections to the DB cluster on the default Aurora port. C. Add an inbound rule to the DB cluster ' s security group. Specify the EC2 instances ' security group as the source over the default Aurora port. This allows connections to the DB cluster from the EC2 instances on the default Aurora port.

https://aws.amazon.com/rds/aurora/global-database/

By adding two more NAT gateways (one per Availability Zone), each private subnet has direct access to a NAT gateway in its own AZ.

This increases network resiliency because even if a single NAT gateway becomes unavailable, the others remain operational, ensuring consistent internet connectivity for the private EC2 instances.

This follows AWS best practices for highly available network architecture in multi-AZ deployments.

This option allows the solutions architect to use an identity policy that grants the user readand write access to their own personal folder on the S3 bucket1. By adding a condition that specifies that the S3 paths must be prefixed with ${aws:username}, the solutions architect can ensure that each scientist can only access their own folder2. By applying the policy on the scientists’ IAM user group, the solutions architect can simplify the management of permissions for all the scientists3. By configuring a trail with AWS CloudTrail to capture all object-level events in the S3 bucket, the solutions architect can record and store information about every action performed on the S3 objects4. By storing the trail output in another S3 bucket, thesolutions architect can secure and archive the log files5. By using Amazon Athena to query the logs and generate reports, the solutions architect can use a serverless interactive query service that can analyze data in S3 using standard SQL.

Identity-based policies

Policy variables

IAM groups

Object-level logging

Creating a trail that applies to all regions

[What is Amazon Athena?]

The best option is to use an Auto Scaling group, a Network Load Balancer, Amazon Route 53, and Amazon DynamoDB to create a scalable, highly available, and low-latency online game application. An Auto Scaling group can automatically adjust the number of EC2 instances based on the demand and traffic in each Region. A Network Load Balancer can distribute the incoming traffic across the EC2 instances and handle millions of requests per second. Amazon Route 53 can use latency-based routing to direct the users to the Region that provides the best performance. Amazon DynamoDB global tables can replicate the game metadata across multiple Regions, ensuring consistency and availability of thedata. This approach minimizes the operational overhead and cost, as it leverages fully managed services and avoids custom logic or replication.

Option A is not optimal because using an EC2 Spot Fleet can introduce the risk of losing the EC2 instances if the Spot price exceeds the bid price, which can affect the availability and performance of the game. Using AWS Global Accelerator can improve the network performance, but it is not necessary if Amazon Route 53 can already route the users to the closest Region. Using Amazon RDS for MySQL can store the game metadata, but it requires setting up read replicas and managing the replication lag across Regions, which can increase the complexity and cost.

Option B is not optimal because using geoproximity routing can direct the users to the Region that is geographically closer, but it does not account for the network latency or performance. Using MySQL databases on EC2 instances can store the game metadata, but it requires managing the EC2 instances, the database software, the backups, the patches, and the replication across Regions, which can increase the operational overhead and cost.

Option D is not optimal because using EC2 Global View is not a valid service. Using a custom DNS server on an EC2 instance can redirect the user to the Region that provides the lowest latency, but it requires developing and maintaining the custom logic, as well as managing the EC2 instance, which can increase the operational overhead and cost. Using Amazon Aurora global database can store the game metadata, but it is more expensive and complex than using Amazon DynamoDB global tables.

Auto Scaling groups

Network Load Balancer

Amazon Route 53

Amazon DynamoDB global tables

A is the correct answer because it uses Amazon DocumentDB (with MongoDB compatibility) as a key-value database that can scale based on demand and supports encryption in transit and at rest. Amazon DocumentDB is a fully managed document database service that is designed to be compatible with the MongoDB API. It is a NoSQL database that is optimized for storing, indexing, and querying JSON data. Amazon DocumentDB supports encryption in transit using TLS and encryption at rest using AWS Key Management Service (AWS KMS). Amazon DocumentDB also supports provisioned IOPS volumes that can scale up to 64 TiB of storage and 256,000 IOPS per cluster. To connect to Amazon DocumentDB, you can use the instance endpoint, which connects to a specific instance in the cluster, or the cluster endpoint, which connects to the primary instance or one of the replicas in the cluster. Using the cluster endpoint is recommended for high availability and load balancing purposes. References:

https://docs.aws.amazon.com/documentdb/latest/developerguide/what-is.html

https://docs.aws.amazon.com/documentdb/latest/developerguide/security.encryption.html

https://docs.aws.amazon.com/documentdb/latest/developerguide/limits.html

https://docs.aws.amazon.com/documentdb/latest/developerguide/connecting.html

" Save your custom error pages in a location that is accessible to CloudFront. We recommend that you store them in an Amazon S3 bucket, and that you don’t store them in the same place as the rest of your website or application’s content. If you store the custom error pages on the same origin as your website or application, and the origin starts to return 5xx errors, CloudFront can’t get the custom error pages because the origin server is unavailable. " https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/GeneratingCustomErrorResponses.html

SCP to deny permissions to administer Private Marketplace to everyone except the role named procurement-manager-role.https://aws.amazon.com/blogs/awsmarketplace/controlling-access-to-a-well-architected-private-marketplace-using-iam-and-aws-organizations/

This approach allows the procurement managers to assume the procurement-manager-role in shared services accounts, which have the AWSPrivateMarketplaceAdminFullAccess managed policy attached to it and can then manage the Private Marketplace. The organization root-level SCP denies the permission to administer Private Marketplace to everyone except the role named procurement-manager-role and another SCP denies the permission to create an IAM role named procurement-manager-role to everyone in the organization, ensuring that only the procurement team can assume the role and manage the Private Marketplace. This approach provides a centralized way to manage and restrict access to Private Marketplace while maintaining a high level of security.

This solution requires minimal operational overhead, as it only requires setting up AWS IoT Core and creating a thing for each device. (Reference: AWS Certified Solutions Architect - Professional Official Amazon Text Book, Page 537)

AWS IoT Core is a fully managed service that enables secure, bi-directional communication between internet-connected devices and the AWS Cloud. It supports the MQTT protocol and includes built-in device authentication and access control. By using AWS IoT Core, the company can easily provision and manage the X.509 certificates for each device, and connect the devices to the service with minimal operational overhead.

The company does not have a data inventory and needs to identify which S3 buckets contain sensitive data. The appropriate AWS managed service for discovering and classifying sensitive data in S3 is Amazon Macie. Macie is designed to discover, classify, and report on sensitive data such as PII in S3 buckets. Amazon Inspector is primarily focused on vulnerability management for compute and container resources and does not provide S3 sensitive data classification in the way Macie does.

After identifying sensitive data locations, the company needs to ensure sensitive data is encrypted with a key that only administrators can access. SSE-S3 uses S3-managed keys and does not provide fine-grained administrative control of key usage in the same way as SSE-KMS with a customer managed key. Using AWS KMS customer managed keys allows the company to control access through key policies and IAM policies so that only designated administrator principals can use or manage the key.

The requirement also implies existing objects already encrypted with SSE-S3 need to be re-encrypted with SSE-KMS for sensitive objects. Changing default encryption only affects new objects. Existing objects must be rewritten (copied over themselves or copied to a new location) using SSE-KMS with the customer managed key. An orchestrated workflow is a common approach to iterate over identified objects and perform copy operations with the desired encryption settings.

Option C uses Macie for discovery, creates a KMS customer managed key restricted to administrators, sets bucket default encryption to SSE-KMS for future objects, and uses a Step Functions workflow to re-encrypt existing sensitive objects. This meets both the discovery requirement and the encryption/control requirement.

Option A is incorrect because Inspector is not the right service to inventory sensitive data in S3. Although the use of a customer managed KMS key and bucket policy enforcement is directionally correct for controlling encryption on writes, the first step (sensitive data discovery) is wrong.

Option B is incorrect because AWS managed keys cannot have their key policies modified by customers in the way customer managed keys can. Also, Inspector is not the right tool for sensitive data discovery in S3.

Option D is incorrect for the same reasons: it relies on Macie correctly for discovery but then attempts to modify an AWS managed key policy, which is not the correct method for restricting access. To restrict access, the company should use a KMS customer managed key with an appropriate key policy.

Therefore, using Amazon Macie plus an AWS KMS customer managed key and a workflow to re-encrypt existing sensitive objects is the correct solution.

The company should purchase the same Reserved Instances for an additional 3-year term with All Upfront payment. The company should purchase a 3-year Compute Savings Plan with All Upfront payment in the management account to cover any additional compute costs. This solution will provide the company with the most cost savings because Reserved Instances and Savings Plans are both pricing models that offer significant discounts compared to On-Demand pricing. Reserved Instances are commitments to use a specific instance type and size in a single Region for a one- or three-year term. You can choose between three payment options: No Upfront, Partial Upfront, or All Upfront. The more you pay upfront, the greater the discount1. Savings Plans are flexible pricing models that offer low prices on EC2 instances, Fargate, and Lambda usage, in exchange for a commitment to a consistent amount of usage (measured in $/hour) for a one- or three-year term. You can choose between two types of Savings Plans: Compute Savings Plans and EC2 Instance Savings Plans. Compute Savings Plans apply to any EC2 instance regardless of Region, instance family, operating system, or tenancy, including those that are part of EMR, ECS, or EKS clusters, or launched by Fargate or Lambda. EC2 Instance Savings Plans apply to a specific instance family within a Region and provide the most savings2. By purchasing the same Reserved Instances for an additional 3-year term with All Upfront payment, the company can lock in the lowest possible price for its EC2 instances that run continuously for 3 years. By purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account, the company can benefit from additional discounts on any other compute usage across its member accounts.

The other options are not correct because:

Purchasing a 1-year Compute Savings Plan with No Upfront payment in each member account would not provide as much cost savings as purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account. A 1-year term offers lower discounts than a 3-year term, and a No Upfront payment option offers lower discounts than an All Upfront payment option. Also, purchasing a Savings Plan in each member account would not allow the company to share the benefits of unused Savings Plan discounts across its organization.

Purchasing a 3-year EC2 Instance Savings Plan with No Upfront payment in the management account to cover EC2 costs in each AWS Region would not provide as much cost savings as purchasing Reserved Instances for an additional 3-year term with All Upfront payment. An EC2 Instance Savings Plan offers lower discounts than Reserved Instances for the same instance family and Region. Also, a No Upfront payment option offers lower discounts than an All Upfront payment option.

Purchasing a 3-year EC2 Instance Savings Plan with All Upfront payment in each member account would not provide as much flexibility or cost savings as purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account. An EC2 Instance Savings Plan applies only to a specific instance family within a Region and does not cover Fargate or Lambda usage. Also, purchasing aSavings Plan in each member account would not allow the company to share the benefits of unused Savings Plan discounts across its organization.

The company wants logging that helps troubleshoot email delivery issues and also wants to search by recipient, subject, and time sent. Amazon SES provides event publishing for email sending and delivery events through configuration sets. With a configuration set, SES can publish sending events such as send, delivery, bounce, complaint, reject, and rendering failure to destinations such as Amazon CloudWatch, Amazon SNS, or Amazon Kinesis Data Firehose. For building a searchable log store, delivering these events into Amazon S3 through Kinesis Data Firehose is an effective approach because S3 provides durable storage and integrates well with query services.

Option A creates an SES configuration set with a Kinesis Data Firehose destination that delivers logs to an S3 bucket. This captures detailed SES event data that is directly useful for troubleshooting delivery issues and retaining historical records for analysis.

Once logs are stored in Amazon S3, Amazon Athena can query the data using SQL. Athena is designed to query data in S3 and is well suited for ad hoc searches. This meets the requirement to search based on recipient, subject, and time sent, assuming the event schema includes these fields (or they are included in the published event payload). Therefore, option C completes the solution by enabling searches over the stored log dataset.

Option B (CloudTrail) records API activity, such as calls made to SES APIs, but it is not designed to capture per-message delivery outcomes (deliveries, bounces, complaints) in a way that supports troubleshooting delivery behavior and detailed email event searching. CloudTrail is useful for auditing who called SES APIs, not for tracking message-level delivery events and outcomes.

Option D (CloudWatch log group) is another valid SES event publishing destination, but if the requirement is to perform flexible searches by multiple dimensions over a potentially large historical dataset, storing the logs in S3 and querying with Athena is a more direct and scalable pattern. Also, the provided option E is incorrect because Athena queries data in S3, not in CloudWatch Logs. CloudWatch Logs has its own query mechanism, but Athena is not used to query CloudWatch Logs directly in the way described.

Option E is incorrect because Amazon Athena does not query CloudWatch Logs as a log store. The typical searchable pattern for Athena is S3-backed datasets.

Therefore, the best combination to satisfy logging and searchable analysis requirements is to publish SES events to S3 via Kinesis Data Firehose (option A) and query those logs with Athena (option C).

This option allows the company to use a private repository in Amazon ECR to store and manage its Docker images securely and efficiently1. By creating a permissions policy for the repository that allows only required ECR operations, such as ecr:GetDownloadUrlForLayer, ecr:BatchGetImage, ecr:BatchCheckLayerAvailability, ecr:PutImage, and ecr:InitiateLayerUpload2, the company can restrict access to the repository and prevent unauthorized actions. By including a condition to allow the ECRoperations if the value of the aws:PrincipalOrgID condition key is equal to the ID of the company’s organization, the company can ensure that only accounts that are within its organization can access the images3. By adding a lifecycle rule to the ECR repository that deletes all untagged images over the count of five, the company can reduce storage costs and retain only the most recent untagged images4.

Amazon ECR private repositories

Amazon ECR repository policies

Restricting access to AWS Organizations members

Amazon ECR lifecycle policies

The correct answer is B.

B. This solution is the most cost-effective because it uses an EC2 Instance Savings Plan for the online gaming application instances, which provides the lowest prices and savings up to 72% compared to On-Demand prices. The EC2 Instance Savings Plan applies to any instance size within the same family and region, regardless of availability zone, operating system, or tenancy. The online gaming application instances run all year and must always be available, so they are not suitable for Spot Instances, which can be interrupted with a two-minute notice. This solution also uses Spot Instances for the analytics application, which can reduce the cost by up to 90% compared to On-Demand prices. The analytics application can be interrupted and resumed without issue, so it is a good fit for Spot Instances, which use spare EC2 capacity.This solution does not require AWSService Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts123

A. This solution is incorrect because it uses On-Demand Instances for the analytics application, which are more expensive than Spot Instances. The analytics application can be interrupted and resumed without issue, so it can benefit from the lower cost of Spot Instances, which use spare EC2 capacity.

C. This solution is incorrect because it uses Spot Instances for the online gaming application, which can be interrupted with a two-minute notice. The online gaming application instances must always be available, so they are not suitable for Spot Instances, which use spare EC2 capacity. This solution also uses AWS Service Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts.

D. This solution is incorrect because it uses On-Demand Instances for the online gaming application, which are more expensive than an EC2 Instance Savings Plan. The online gaming application instances run all year and must always be available, so they are suitable for an EC2 Instance Savings Plan, which provides the lowest prices and savings up to 72% compared to On-Demand prices. This solution also uses AWS Service Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts.

A is correct becausedefault AWS-managed KMS keys cannot be shared across accounts. Therefore, the only way to enable cross-account backup is to restore each RDS instance using acustomer-managed key (CMK)andshare that keywith the central account.

Then, AWS Backup can performcross-account backup operationsusing AWS Organizations trusted access and backup policies.

Option B is incorrect due to theinability to share default keys.

Option C and D are technically invalid — RDS snapshots cannot be decrypted or re-encrypted manually.

The correct answer is B. The requirement is resource-side protection for all current and future private Amazon ECR repositories across the organization. AWS Organizations resource control policies are designed to centrally restrict permissions on resources in member accounts and are appropriate when external principals must be prevented from accessing organization-owned resources. AWS documentation lists Amazon Elastic Container Registry as a supported service for RCPs. A condition such as aws:PrincipalOrgID enforces that only principals from the same AWS Organization can access the resources. SCPs restrict principals in accounts, not resource exposure to external principals. Modifying IAM policies in hundreds of accounts is operationally poor and does not protect future repository policies consistently. AWS RAM is not the right control plane for private ECR repository access. (AWS Documentation)

===============

AWS Elastic Disaster Recovery (AWS DRS) is a service that minimizes downtime and data loss with fast, reliable recovery of on-premises and cloud-based applications using affordable storage, minimal compute, and point-in-time recovery1. Users can set up AWS DRS on their source servers to initiate secure data replication to a staging area subnet in their AWS account, in the AWS Region they select. Users can then launch recovery instances on AWS within minutes, using the most up-to-date server state or a previous point in time.

To configure a cloud backup of the application with AWS DRS, users need to create a VPC that has at least two public subnets, a virtual private gateway, and an internet gateway. AVPC is a logically isolated section of the AWS Cloud where users can launch AWS resources in a virtual network that they define2. A public subnet is a subnet that has a route to an internet gateway3. A virtualprivate gateway is the VPN concentrator on the Amazon side of the Site-to-Site VPN connection4. An internet gateway is a horizontally scaled, redundant, and highly available VPC component that allows communication between instances in the VPC and the internet. Users need to create at least two public subnets for redundancy and high availability. Users need to create a virtual private gateway and attach it to the VPC to enable VPN connectivity between the on-premises network and the target AWS network. Users need to create an internet gateway and attach it to the VPC to enable internet access for the replication servers.

To ensure that replication traffic does not travel through the public internet, users need to create an AWS Direct Connect connection and a Direct Connect gateway between the on-premises network and the target AWS network. AWS Direct Connect is a service that establishes a dedicated network connection from an on-premises network to one or more VPCs. A Direct Connect gateway is a globally available resource that allows users to connect multiple VPCs across different Regions to their on-premises networks using one or more Direct Connect connections. Users need to create an AWS Direct Connect connection between their on-premises network and an AWS Region. Users need to create a Direct Connect gateway and associate it with their VPC and their Direct Connect connection.

To ensure that the application is not accessible from the internet, users need to select the option to use private IP addresses for data replication during configuration of the replication servers. This option configures the replication servers with private IP addresses only, without assigning any public IP addresses or Elastic IP addresses. This way, the replication servers can only communicate with other resources within the VPC or through VPN connections.

Option A is incorrect because creating a VPC that has at least two private subnets, two NAT gateways, and a virtual private gateway is not necessary or cost-effective. A private subnet is a subnet that does not have a route to an internet gateway3. A NAT gateway is a highly available, managed Network Address Translation (NAT) service that enables instances in a private subnet to connect to the internet or other AWS services, but prevents the internet from initiating connections with those instances. Users do not need to createprivate subnets or NAT gateways for this use case, as they can use public subnets with private IP addresses for data replication.

Option C is incorrect because creating an AWS Site-to-Site VPN connection between the on-premises network and the target AWS network will not ensure that replication traffic does not travel through the public internet. A Site-to-Site VPN connection consists of two VPN tunnels between an on-premises customer gateway device and a virtual private gateway in your VPC4. The VPN tunnels are encrypted using IPSec protocols, but they still use public IP addresses for communication. Users need to use AWS Direct Connect instead of Site-to-Site VPN for this use case.

Option F is incorrect because selecting the option to ensure that the Recovery instance’s private IP address matches the source server’s private IP address during configuration of the launch settings for the target servers will not ensure that the application is not accessible from the internet. This option configures the Recovery instance with an identical private IP address as its source server when launched in drills or recovery mode. However, this option does not prevent assigning public IP addresses or Elastic IP addresses to the Recovery instance. Users need to select the option to use private IP addresses for data replication instead.

it covers all the requirements mentioned in the question, it will allow collecting the detailed metrics, including process information and it provides a way to query and analyze the data using Amazon Athena.

Store the PGP Private Key:

Step 1: In the AWS Management Console, navigate to AWS Secrets Manager.

Step 2: Store the PGP private key in Secrets Manager. Ensure the key is encrypted and properly secured.

Set Up the Transfer Family Managed Workflow:

Step 1: In the AWS Transfer Family console, create a new managed workflow.

Step 2: Add a nominal step to the workflow that includes the decryption of the files. Configure this step with the PGP decryption parameters, referencing the PGP private key stored in Secrets Manager.

Step 3: Associate this workflow with the Transfer Family SFTP server, ensuring that incoming files are automatically decrypted upon receipt.

This solution ensures that the data is securely decrypted as it is transferred from the SFTP server to the S3 bucket, automating the decryption process and leveraging AWS Secrets Manager for key management.

References

AWS Transfer Family Documentation

Using AWS Secrets Manager for Managing Secrets

AWS Transfer Family Managed Workflows

The best solution is to deploy an Amazon RDS instance with a cross-Region read replica in a secondary Region. This will provide the company with a database solution that can fail over to the secondary Region in case of a disaster. The read replica will have minimal replication lag and can be promoted to become the primary in less than 10 minutes, meeting the RTO requirement. The RPO requirement of less than 5 minutes can also be met by using synchronous replication within the primary Region and asynchronous replication across Regions. This solution will also have the lowest cost compared to the other options, as it does not involve additional services or resources. References: [Amazon RDS User Guide], [Amazon Aurora User Guide]

https://docs.aws.amazon.com/vm-import/latest/userguide/vmimport-image-import.html

- Export an OVF Template

- Create / use an Amazon S3 bucket for storing the exported images. The bucket must be in the Region where you want to import your VMs.

- Create an IAM role named vmimport.

- You ' ll use AWS CLI to run the import commands.

https://aws.amazon.com/premiumsupport/knowledge-center/import-instances/

Provisioned concurrencyaddresses cold starts and improves performance during spikes.

RDS Reserved Instancessave money over On-Demand for predictable usage.

AWS WAFis designed to protect againstSQL injectionandweb exploits, and integrates with CloudFront.

AWS Lambda Provisioned Concurrency

AWS WAF Web ACL with CloudFront

You can use the management account of the organization in AWS Billing and Cost Management console to turn off RI sharing for the HR department ' s production AWS account. This will prevent other departments from sharing the RI discounts and ensure that only the HR department can use the RIs purchased in their production account.

https://aws.amazon.com/es/blogs/mt/implement-aws-resource-tagging-strategy-using-aws-tag-policies-and-service-control-policies-scps/

https://docs.aws.amazon.com/autoscaling/ec2/userguide/create-asg-instance-type-requirements.html#use-attribute-based-instance-type-selection-prerequisites

Create an S3 Bucket:

Navigate to Amazon S3 in the AWS Management Console and create a new S3 bucket to store the game files.Enable static website hosting on this bucket.

Upload Game Files:

Upload the 5 GB game release package to the S3 bucket. Ensure that the files are publicly accessible if required for download.

Configure Amazon Route 53:

Set up a new domain or subdomain in Amazon Route 53 and point it to the S3 bucket. This allows users to access the game files using a custom URL.

Use Amazon CloudFront:

Create a CloudFront distribution with the S3 bucket as the origin. CloudFront is a content delivery network (CDN) that caches content at edge locations worldwide, improving download performance and reducing latency for users regardless of their location.

Publish the Download URL:

Use the CloudFront distribution URL as the download link for users to access the game files. CloudFront will handle the efficient distribution and caching of the content.

This solution leverages the scalability of Amazon S3 and the performance benefits of CloudFront to provide an optimal download experience for users globally while minimizing costs.

References

Amazon CloudFront Documentation

Amazon S3 Static Website Hosting

This solution requires the least amount of effort as it only requires to update the API endpoint to private in API Gateway and create an interface VPC endpoint. Then create a resource policy and attach it to the API. This will make the API only accessible from the VPC and still keep the authentication mechanism intact. Reference:

https://aws.amazon.com/premiumsupport/knowledge-center/private-api-gateway-vpc-endpoint/

https://aws.amazon.com/api-gateway/features/

The most secure and manageable solution is to useVPC interface endpointsforS3andSystems Manager, and establishVPC peeringto privately access the patch repo in the core account.

Systems Manager VPC setup

CloudFormation cannot delete non-empty S3 buckets. OptionAallows you to create acustom Lambda resourcethat deletes all objects in the S3 bucket before the stack deletes it. The DependsOn ensures the bucket deletion occurs only after the Lambda has completed.

B: Adding DeletionPolicy: Delete does not resolve the issue if the bucket still contains objects.

C: Snapshot doesn ' t apply to S3 and won ' t help here.

D: Changing to Amazon EFS would require architectural changes, which are not allowed per requirements.

The key requirement is to provide access to a private Git repository while keeping the SageMaker notebook instances isolated from the internet. The environment is intentionally configured without internet access, and connectivity to AWS services is provided through VPC endpoints. Introducing a NAT gateway and routing to the internet would violate the requirement to keep the notebook instances isolated from the internet, and network ACLs cannot reliably restrict outbound access by URL or domain name because NACLs operate at the IP and port level and do not provide DNS-aware URL filtering.

SageMaker supports integrating Git repositories so that notebooks can pull code directly as part of the workflow. When using a private repository, credentials must be handled securely. Using AWS Secrets Manager to store and reference Git credentials allows authentication without embedding usernames and passwords in code or URLs and without granting broad network access. This approach meets the requirement with low operational overhead because it relies on managed SageMaker Git integration and managed secret storage rather than custom networking controls.

Option A uses SageMaker’s Git repository integration with the remote URL and stores credentials in AWS Secrets Manager. This allows notebooks to access the private repository in a controlled, auditable way while preserving the “no internet access” posture of the VPC design (because it does not require adding a NAT gateway or public routes).

Option B is not appropriate because embedding credentials (even just usernames) in URLs is not a secure or robust credential management practice. It also does not solve private authentication in a secure manner and can lead to credential leakage through logs or configuration.

Options C and D are not correct because they require adding a NAT gateway and routing to the internet. That directly conflicts with the requirement that SageMaker notebook instances remain isolated from the internet. Additionally, the proposed restriction mechanism is invalid: network ACLs cannot restrict traffic to a specific URL. They only allow/deny traffic based on IP addresses, ports, and protocols. A Git repository can resolve to multiple IPs, can change IPs, and can use CDNs, making NACL-based IP allowlisting brittle and operationally heavy.

Therefore, integrating the Git repository with SageMaker and using Secrets Manager for credentials is the best solution with the least operational overhead while maintaining internet isolation.

Option C is correct because hosting the web application in Amazon S3, storing the uploaded videos in Amazon S3, and using S3 event notifications to publish events to the SQS queue reduces the operational overhead of managing EC2 instances and EBS volumes. Amazon S3 can serve static content such as HTML, CSS, JavaScript, and media files directly from S3 buckets. Amazon S3 can also trigger AWS Lambda functions through S3 event notifications when new objects are created or existing objects are updated or deleted. AWS Lambda can process the SQS queue with an AWS Lambda function that calls the Amazon Rekognition API to categorize thevideos. This solution eliminates the need for custom recognition software and third-party dependencies345

https://repost.aws/knowledge-center/vpc-ip-address-range

For an EC2 instance to appear as a managed instance in AWS Systems Manager, several prerequisites must be met. Two of the most fundamental requirements are that the Systems Manager Agent (SSM Agent) is installed and running on the instance, and that the instance has an IAM role attached that grants the necessary permissions for Systems Manager to communicate with the service.

When importing a VM from an on-premises environment, the resulting AMI might not include the SSM Agent by default, or the agent might not be enabled to start automatically. If the agent is missing or not running, the instance cannot register with Systems Manager, and it will not appear in the Systems Manager console as a managed instance. Therefore, verifying that the SSM Agent is installed and running is a primary troubleshooting step.

Additionally, the EC2 instance must have an IAM instance profile (role) attached that includes the permissions required by Systems Manager. Typically, this is achieved by attaching a role that includes the AmazonSSMManagedInstanceCore managed policy. Without the appropriate IAM role, even a correctly installed and running agent will not be able to register the instance or perform Systems Manager operations.

Option C (VPC endpoint existence) is not required in this scenario because the instance is in a public subnet and has a public IP address. In this case, the instance can communicate with the Systems Manager endpoints over the internet. VPC endpoints are required when instances do not have internet access (for example, in private subnets without NAT or public IPs), which is not the case here.

Option D (Application Discovery Agent) is unrelated. The AWS Application Discovery Agent is used for migration assessment and discovery of on-premises workloads, not for Systems Manager managed instance registration.

Option E (service-linked roles) is generally not a common cause for a single instance failing to appear as managed, especially when other Systems Manager functionality is working in the account. Service-linked roles for Systems Manager are usually created automatically when needed and do not typically prevent an individual instance from registering if the agent and instance role are correctly configured.

Therefore, the correct troubleshooting steps are to verify the presence and operation of the Systems Manager Agent and to ensure the EC2 instance has the correct IAM role attached.

https://docs.aws.amazon.com/apigateway/latest/developerguide/dns-failover.html

The correct answer is D. Use a scheduled scaling policy for the EC2 instances. Host the database on an Amazon Aurora PostgreSQL Multi-AZ DB cluster. Create an Amazon EventBridge rule that invokes an AWS Lambda function to create a larger Aurora Replica before a sale event. Fail over to the larger Aurora Replica. Create another EventBridge rule that invokes another Lambda function to scale down the Aurora Replica after the sale event.

This solution will meet the requirements of maximizing availability during the sale events. A scheduled scaling policy for the EC2 instances will allow the application to scale up and down according to the predefined schedule of the sale events. Hosting the database on an Amazon Aurora PostgreSQL Multi-AZ DB cluster will provide high availability and durability, as well as compatibility with PostgreSQL. Creating an Amazon EventBridge rule that invokes an AWS Lambda function to create a larger Aurora Replica before a sale event will ensure that the database can handle the increased read traffic during the peak periods. Failing over to the larger Aurora Replica will make it the primary instance, which will also improve the write performance of the database. Creating another EventBridge rule that invokes another Lambda function to scale down the Aurora Replica after the sale event will reduce the cost and resources of the database.

To effectively gather information about network dependencies between VMs in an on-premises data center for migration to AWS, it ' s crucial to use tools that can capture detailed application and server dependencies. The AWS Application Discovery Service is designed for this purpose, particularly when migrating from environments like Linux-based VMware VMs. By installing the AWS Application Discovery Agent on the on-premises servers, the service can collect necessary data such as host IP addresses, hostnames, and network connection information. This data is crucial for creating a comprehensive network diagram that outlines the interactions and dependencies between various components of the on-premises infrastructure. The integration with AWS Migration Hub enhances this process by allowing the visualization of these dependencies in a network diagram format, aiding in the planning and execution of the migration process. This approach ensures a thorough understanding of the on-premises environment, which is essential for a successful migration to AWS.

https://aws.amazon.com/solutions/implementations/automations-for-aws-firewall-manager/

In this solution, AWS Firewall Manager is used to manage AWS WAF rules across accounts in the organization. An AWS Systems Manager Parameter Store parameter is used to store account numbers and OUs to manage. This parameter can be updated as needed to add or remove accounts or OUs. An Amazon EventBridge rule is used to identify any changes to the parameter and to invoke an AWS Lambda function to update the security policy in the Firewall Manager administrative account. This solution allows for easy management of AWS WAF rules across multiple accounts with minimal operational overhead

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_inheritance_auth.html

Create an IAM role in the sales account and grant access to the S3 bucket. From the marketing account, assume the IAM role in the sales account to access the S3 bucket. Update the QuickSight role, to create a trust relationship with the new IAM role in the sales account.

This approach is the most secure way to grant cross-account access to the data in the S3 bucket while minimizing operational overhead. By creating an IAM role in the sales account, the marketing team can assume the role in their own account, and have access to the S3 bucket. And updating the QuickSight role, to create a trust relationship with the new IAM role in the sales account will grant the marketing team to access the data in the S3 bucket and use it for data visualization using QuickSight.

AWS Resource Access Manager (AWS RAM) also allows sharing of resources between accounts, but it would require additional management and configuration to set up the sharing, which would increase operational overhead.

Using S3 replication would also replicate the data to the marketing account, but it would not provide the marketing team access to the original data, and also it would increase operational overhead with managing the replication process.

IAM roles and policies, KMS grants and trust relationships are a powerful combination for managing cross-account access in a secure and efficient manner.

Using an SQS queue to store events and invoke the Lambda function will decouple the third-party service calls and ensure that all the calls are eventually completed. SQS allows you to store messages in a queue and process them asynchronously, which eliminates the need for the application to wait for a response from the third-party service. The messages will be stored in the SQS queue until they are processed by the Lambda function, even if the Lambda function is currently unavailable or busy. This will ensure that all the calls are eventually completed, even if there are delays or errors.

AWS Step Functions state machines can also be used to pass events to the Lambda function, but it would require additional management and configuration to set up the state machine, which would increase operational overhead.

Amazon EventBridge rule can also be used to pass events to the Lambda function, but it would not provide the same level of decoupling and reliability as SQS.

Using Amazon Simple Notification Service (Amazon SNS) topic to store events and Invoke the Lambda function, is similar to SQS, but SNS is a publish-subscribe messaging service and SQS is a queue service. SNS is used for sending messages to multiple recipients, SQS is used for sending messages to a single recipient, so SQS is more appropriate for this use case.

The company should configure Amazon FSx for Lustre with an import and export policy. The company should link the new file system to an S3 bucket. The company should install the Lustre client and mount the document store to an Amazon EC2 instance by using NFS. This solution will meet the requirements with the least amount of effort because Amazon FSx for Lustre is a fully managed service that provides a high-performance filesystem optimized for fast processing of workloads such as machine learning, highperformance computing, video processing, financial modeling, and electronic design automation1. Amazon FSx for Lustre can be linked to an S3 bucket and can import data from and export data to the bucket2. The import and export policy can be configured to automatically import new or changed objects from S3 and export new or changed files to S33. This will ensure that the files are available to the public for download within 30 minutes. Amazon FSx for Lustre supports NFS version 3.0 protocol for Linux clients.

The other options are not correct because:

Migrating the application to an AWS Lambda function would require a lot of effort and may not be feasible for the existing server that generates many documents. Lambda functions have limitations on execution time, memory, disk space, and network bandwidth.

Setting up an Amazon S3 File Gateway would not work because S3 File Gateway does not support write-back caching, which means that files written to the file share are uploaded to S3 immediately and are not available locally until they are downloaded again. This would not provide fast local access to the files that the server generates and modifies.

Configuring AWS DataSync to connect to an Amazon EC2 instance would not meet the requirement of making the files available to the public for download within 30 minutes. DataSync is a service that transfers data between on-premises storage systems and AWS storage services over the internet or AWS Direct Connect. DataSync tasks can be scheduled to run at specific times or intervals, but they are not triggered by file changes.

B is correct. WhenPrivate DNSis enabled for an interface endpoint, AWS automatically updates the public service DNS names (e.g., s3.amazonaws.com) to resolve toprivate IPsinside the VPC. This ensures all traffic stays within the AWS network and complies with the private IP policy.

A is not necessary — VPC endpoints already route via local network.

C only affects security group rules, not DNS resolution.

D is unnecessary unless using custom DNS resolution systems.

Set Up EventBridge Event Bus:

Step 1: Open the Amazon EventBridge console and create a custom event bus. This bus will be used to handle user deletion events.

Step 2: Name the event bus appropriately (e.g.,user-deletion-bus).

Post Events on User Deletion:

Step 1: Modify the central user service to post an event to the custom EventBridge event bus whenever a user is deleted.

Step 2: Ensure the event includes relevant details such as the user ID and any other necessary metadata.

Create EventBridge Rules for Microservices:

Step 1: For each microservice that needs to delete user data, create a new rule in EventBridge that triggers on the user deletion event.

Step 2: Define the event pattern to match the user deletion event. This pattern should include the event details posted by the central user service.

Invoke Microservice Logic:

Step 1: Configure the EventBridge rule to invoke a target, such as an AWS Lambda function, which contains the logic to delete the user data from the microservice ' s data store.

Step 2: Each microservice should have its Lambda function or equivalent logic to handle the deletion of user data upon receiving the event.

Using Amazon EventBridge ensures a scalable, reliable, and decoupled approach to handle the deletion of user data across multiple microservices. This setup allows each microservice to independently process user deletion events without direct dependencies on other services.

References

AWS EventBridge Documentation

DynamoDB Streams and AWS Lambda Triggers

Implementing the Transactional Outbox Pattern with EventBridge Pipes​(AWS Documentation)​​(Amazon Web Services, Inc.)​​(Amazon Web Services, Inc.)​​(AWS Documentation)​​(AWS Cloud Community)​.

The company’s goal is to modernize the application while minimizing operational overhead. The current architecture relies heavily on self-managed EC2 instances for web serving, APIs, search, and relational data storage. This increases operational burden related to patching, scaling, availability, and troubleshooting.

Option B replaces self-managed components with fully managed AWS services that are purpose-built for each workload type. Hosting the static web application on Amazon S3 with Amazon CloudFront removes the need to manage web servers such as NGINX and provides global content delivery with built-in scalability and high availability. Migrating the search data to Amazon OpenSearch Service eliminates the need to manage an EC2-based OpenSearch node, providing a managed search service with built-in scaling, patching, and monitoring. Migrating PostgreSQL to Amazon Aurora PostgreSQL removes the operational overhead of managing database instances on EC2 and provides managed backups, high availability, and performance optimizations. Running APIs on AWS App Runner further reduces operational effort by abstracting infrastructure management, scaling, and deployment, allowing the team to focus on application code.

Option A still requires running and operating containerized databases and search engines, which is not recommended and does not significantly reduce operational overhead compared to EC2-based deployments. Databases and OpenSearch are better consumed as managed services rather than as containers.

Option C introduces Amazon EKS, which adds significant operational complexity. Even with managed node groups, Kubernetes requires cluster management, networking configuration, upgrades, and ongoing operational expertise. This contradicts the requirement for the least operational overhead.

Option D attempts to run databases and OpenSearch on AWS App Runner. App Runner is designed for stateless web services and APIs, not for stateful services such as databases or search engines. This architecture is not aligned with AWS best practices and would introduce reliability and operational challenges.

Therefore, using managed services such as Amazon CloudFront, Amazon S3, Amazon OpenSearch Service, Amazon Aurora PostgreSQL, and AWS App Runner provides the most modern architecture with the least operational overhead.

https://aws.amazon.com/premiumsupport/knowledge-center/vpc-analyze-inbound-traffic-nat-gateway/ by Cloudxie says " select appropriate log "

This option allows the solution to decouple the API from the Lambda function and use EventBridge as an event-driven service that can handle failures gracefully1. By using two event buses, one for normal events and one for failed events, the solution can ensure that no data is lost and that data can be processed later if failures occur2. The primary event bus receives the data from the weather stations through the API integration and triggers the Lambda function through a rule. The Lambda function can then call the third-party service for data pre-processing. If the third-party service fails, the Lambda function can send an error response to EventBridge, which will route it to the secondary event bus as a failure destination3. The secondary event bus can then store the failed events in another service, such as Amazon S3 or Amazon SQS, for troubleshooting or reprocessing.

Using Amazon EventBridge with AWS Lambda

Using multiple event buses

Using failure destinations

[Using dead-letter queues]

==================

The correct answer is B. This option uses IAM Identity Center to create permission sets that map to the existing IAM roles in each account. This way, the company can leverage the existing policies and roles that are already configured for the expected activities of each group of users in each account. The company also needs to create a customer managed policy that allows the group to assume the roles and attach it to thepermission set. This policy grants the necessary permissions for IAM Identity Center to assume the roles on behalf of the users. Finally, the company can assign user access to the AWS accounts in IAM Identity Center, which will automatically create IAM users and groups in each account based on the permission sets.

Option A is incorrect because it requires creating new policies in each account and giving them the same name. This is not necessary and adds complexity and overhead. The company can use the existing IAM roles and policies that are already configured for each account.

Option C is incorrect because it requires creating new policies in each account and giving them unique names. This is also not necessary and adds complexity and overhead. The company can use the existing IAM roles and policies that are already configured for each account.

Option D is incorrect because it requires adding the OU to the accounts configuration in IAM Identity Center. This is not supported by IAM Identity Center, which only allows adding individual accounts or all accounts in an organization.

This option will meet the requirements of preventing any new or existing EC2 instances in the OU’s accounts from gaining a public IP address. An SCP is a policy that you can attach to an OU or an account in AWS Organizations to define the maximum permissions for the entities in that OU or account. By creating an SCP that denies the ec2:RunInstances and ec2:AssociateAddress actions when the value of the aws:RequestTag/aws:PublicIp condition key is true, you can prevent any user or role in the OU from launching instances that have a public IP address or attaching a public IP address to existing instances. This will effectively enforce a security best practice and reduce the risk of unauthorized access to your EC2 instances.

Using Amazon API Gateway with an SQS queue ensures all data sent to the API is durably stored for processing, even during traffic surges.

API Gateway service integration with SQS provides a managed, serverless ingestion layer that absorbs unpredictable traffic bursts without data loss.

The AWS Lambda function processes messages from SQS, providing scalable, on-demand compute to handle environmental data efficiently.

This solution aligns with AWS best practices for building resilient, serverless, event-driven architectures that can handle high-volume, bursty workloads.

This solution will meet the requirements of making the application highly available with the least development time. Creating a new AMI that is configured with SSM Agent will enable the company to use AWS Systems Manager to manage and patch the EC2 instances automatically, reducing downtime and human errors. Using a launch template for an Auto Scaling group will allow the company to launch multiple instances of the same configuration and scale them up or down based on demand. Using smaller instances in the Auto Scaling group will reduce the cost and improve the performance of the application tier. Creating an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group will increase the availability and fault tolerance of the application tier. Migrating the database to Amazon Aurora MySQL will provide a fully managed, compatible, and scalable relational database service that can handle high throughput and concurrent connections.

Deploying the shared libraries and custom classes to a Docker image and uploading the image to Amazon Elastic Container Registry (Amazon ECR) and creating a Lambda layer that uses the Docker image as the source. Then, deploying the API ' s Lambda functions as Zip packages and configuring the packages to use the Lambda layer would meet the requirements for simplifying the deployment and optimizing for code reuse.

A Lambda layer is a distribution mechanism for libraries, custom runtimes, and other function dependencies. It allows you to manage your in-development function code separately from your dependencies, this way you can easily update your dependencies without having to update your entire function code.

By deploying the shared libraries and custom classes to a Docker image and uploading the image to Amazon Elastic Container Registry (ECR), it makes it easy to manage and version the dependencies. This way, the company can use the same version of the dependencies across different Lambda functions.

By creating a Lambda layer that uses the Docker image as the source, the company can configure the API ' s Lambda functions to use the layer, reducing the need to include the dependencies in each function package, and making it easy to update the dependencies across all functions at once.

Cross region with AWS Backup:https://docs.aws.amazon.com/aws-backup/latest/devguide/cross-region-backup.html

S3 Transfer Acceleration is a feature that enables fast, easy, and secure transfers of files over long distances between your client and an S3 bucket1. It works by leveraging the CloudFront edge network to route your requests to S3 over an optimized network path1. By using a Transfer Acceleration endpoint when generating a presigned URL, you can allow authenticated users to upload objects fasterand more reliably2. Additionally, using the S3 multipart upload API can improve the performance of large object uploads by breaking them into smaller parts and uploading them in parallel3.

S3 Transfer Acceleration

Using Transfer Acceleration with presigned URLs

Uploading objects using multipart upload API

https://repost.aws/knowledge-center/ecs-fargate-service-auto-scaling

an AWS Lambda function in the backup region to promote the read replica and modify the Auto Scaling group values, and then configuring Route 53 with a health check that monitors the web application and sends an Amazon SNS notification to the Lambda function when the health check status is unhealthy. Finally, the application ' s Route 53 record should be updated with a failover policy that routes traffic to the ALB in the backup region when a health check failure occurs. This approach provides automatic failover to the backup region when a health check failure occurs, reducing the RTO to less than 15 minutes. Additionally, this approach is cost-effective as it does not require an active-active strategy.

The requirements specify cross-Region failover with an RPO of 5 minutes and an RTO of 20 minutes for a large (10 TB) database. Achieving a low RPO in a secondary Region typically requires near-continuous replication, not periodic snapshots. Achieving a 20-minute RTO requires that the standby data store already exists in the secondary Region and can be promoted quickly.

A cross-Region read replica provides continuous asynchronous replication from the primary Region to a replica in the secondary Region. This is the standard managed approach for cross-Region disaster recovery for Amazon RDS engines that support read replicas. With a read replica already running in the secondary Region, failover can be accomplished by promoting the replica to a standalone primary database instance. This supports an RTO target like 20 minutes because the data is already present and the promotion operation is typically faster than restoring from a full snapshot, especially at 10 TB scale.

Option B describes creating an RDS Multi-AZ DB cluster in the primary Region and a cross-Region read replica in the secondary Region. Multi-AZ addresses high availability within the primary Region, while the cross-Region replica addresses disaster recovery. The option also includes automation using CloudWatch alarms and a Lambda function to promote the replica when a failure is detected. This supports the required RTO by reducing manual intervention time and supports the RPO by using continuous replication.

Option A is not practical for RPO 5 minutes at 10 TB. Taking and copying snapshots every 5 minutes would be operationally heavy and would not complete within the required window; snapshot creation and cross-Region copy are not designed for such high-frequency near-real-time DR. It also risks missing the RPO due to snapshot completion time.

Option C introduces unnecessary complexity and additional services. Creating a second cluster only at failure time is not compatible with a 20-minute RTO for a 10 TB database, and using DMS for continuous sync is more development and operational work than using native RDS replication. This is not the least overhead or most direct DR design.

Option D is incorrect as stated because cross-Region read replicas are not automatically failed over and promoted by an “automated failover” feature in the way Multi-AZ failover works within a Region. Automated backups do not provide near-real-time cross-Region recovery to meet a 5-minute RPO. The usual approach for cross-Region is to create the replica and then promote it through a controlled process, which can be automated via monitoring and Lambda as in option B.

Therefore, using a cross-Region read replica with automation to promote it provides the necessary RPO/RTO with a managed service approach.

Amazon EC2 Spot Instances offer spare compute capacity at steep discounts compared to On-Demand prices. Spot Instances can be interrupted by EC2 with two minutes of notification when EC2 needs the capacity back. Amazon EMR can handle Spot interruptions gracefully by decommissioning the nodes and redistributing the tasks to other nodes. By launching all nodes on Spot Instances in an instance fleet, the solutions architect can minimize the compute costs of the EMR cluster. An instance fleet is a collection of EC2 instances with different types and sizes that EMR automatically provisions to meet a defined target capacity. By terminating the cluster when the processing is completed, the solutions architect can avoid paying for idle resources. References:

https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-managed-scaling.html

https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-instance-fleet.html

https://aws.amazon.com/blogs/big-data/optimizing-amazon-emr-for-resilience-and-cost-with-capacity-optimized-spot-instances/

Evaluate Target Health Setting:

Ensure that the " Evaluate Target Health " setting is enabled for the latency alias resource record sets in Route 53. This setting helps Route 53 determine the health of the resources associated with the alias record and redirect traffic appropriately.

HTTP Health Checks:

Configure HTTP health checks for all weighted resource record sets. Health checks monitor the availability and performance of the web servers, allowing Route 53 to reroute traffic to healthy servers in case of a failure.

Verify that the health checks are correctly set up and associated with the resource record sets. This ensures that Route 53 can detect server failures and redirect traffic to the servers in the other Region.

By enabling the " Evaluate Target Health " setting and configuring HTTP health checks, Route 53 can effectively manage traffic during failover scenarios, ensuring high availability and reliability.

References

AWS Route 53 Documentation on Latency-Based Routing【50】.

AWS Architecture Blog on Cross-Account and Cross-Region Setup【49】.

The company has many S3 buckets and many identities across multiple AWS accounts. It currently uses S3 bucket policies for granular authorization. As requirements grow, the bucket policies have become very large and have hit the maximum size limits. The company needs a way to express granular, identity-based access to S3 resources without relying on increasingly complex bucket policies.

AWS provides S3 Access Grants to centrally manage S3 data access based on identities. S3 Access Grants integrate with IAM Identity Center and external identity providers such as Microsoft Entra ID (formerly Azure AD). With S3 Access Grants, you define grants that associate specific identities or groups (from IAM Identity Center) with permissions on specific S3 buckets, prefixes, or objects. Access Grants maintain a mapping between identities and S3 resources without requiring large, per-bucket policies.

Option A describes exactly this approach. You create an S3 Access Grant configuration associated with the IAM Identity Center instance that is already integrated with the company’s IdP. You then define S3 Access Grants for the various user groups according to business requirements, specifying the appropriate S3 buckets and prefixes. When users access S3 through AWS tools that support S3 Access Grants, temporary credentials are issued that reflect the grants. This centralizes authorization management, reduces the size and complexity of S3 bucket policies, and minimizes operational overhead because you manage permissions per identity group rather than constantly editing long bucket policies.

Option B uses S3 access points and Object Lambda Access Points with a Lambda function for data filtering. This is more suitable for content transformation or redaction rather than primary authorization management. It adds operational complexity by requiring custom Lambda code and Object Lambda configuration, and it does not directly solve the problem of large bucket policies for fine-grained permissions.

Option C uses per-bucket S3 access points with attached policies. While access points can simplify some aspects of access control and allow per-application policies, using many access points with detailed policies can still lead to significant policy complexity. It also does not take advantage of the existing IAM Identity Center integration and does not centrally align permissions with identity groups.

Option D uses AWS Organizations service control policies (SCPs). SCPs are intended to define high-level guardrails and maximum permissions at the account or organizational unit level, not detailed, per-bucket or per-prefix access control for individual users or groups. Using SCPs to express granular S3 bucket permissions would be complex, hard to maintain, and not aligned with the purpose of SCPs.

Therefore, using S3 Access Grants integrated with IAM Identity Center (option A) provides a centralized, identity-aware, low-overhead way to manage granular S3 access without hitting S3 bucket policy size limits.

Automating the process of increasing service quotas for Amazon EC2 instances in new AWS accounts with minimal operational overhead can be effectively achieved by using Amazon EventBridge, Amazon SNS, and AWS Lambda. An EventBridge rule can detect the creation of a new account and trigger an SNS topic, which in turn invokes a Lambda function. This function can then programmatically request a service quota increase for EC2 instances using the AWS Service Quotas API. This approach streamlines the process, reduces manual intervention, and ensures that new accounts are automatically configured with the desired service quotas.

Amazon EventBridge Documentation: Provides guidance on setting up event rules for detecting AWS account creation.

AWS Lambda Documentation: Details how to create and configure Lambda functions to perform automated tasks, such as requesting service quota increases.

AWS Service Quotas Documentation: Offers information on managing and requesting increases for AWS service quotas programmatically.

Establish AWS Direct Connect Connections:

Step 1: Set up two AWS Direct Connect (DX) connections from the company headquarters to a chosen AWS Region. This provides a redundant and high-availability setup to ensure continuous connectivity.

Step 2: Ensure that these DX connections terminate in a specific Direct Connect location associated with the chosen AWS Region.

Use Company WAN:

Step 1: Configure the company ' s global WAN to route traffic through the established Direct Connect connections.

Step 2: This setup ensures that all traffic between the company ' s headquarters and AWS does not traverse the public internet, maintaining compliance with security requirements.

Set Up Direct Connect Gateway:

Step 1: Create a Direct Connect Gateway in the AWS Management Console. This gateway allows you to connect your Direct Connect connections to multiple VPCs across different AWS Regions.

Step 2: Associate the Direct Connect Gateway with the VPCs in the various Regions where your critical data is stored. This enables access to data in multiple Regions through a single Direct Connect connection.

By using Direct Connect and Direct Connect Gateway, the company can achieve secure, reliable, and cost-effective access to data stored across multiple AWS Regions without using the public internet, ensuring compliance with industry regulations.

References

AWS Direct Connect Documentation

Building a Scalable and Secure Multi-VPC AWS Network Infrastructure​(AWS Documentation)​​(AWS Documentation)​.

A is correct because the agent performs autonomous workload actions without user interaction. In OAuth terms, this maps to the client credentials grant, where the workload authenticates as itself rather than on behalf of an interactive user. Amazon Bedrock AgentCore Identity is designed for identity and credential management for AI agents and automated workloads, including workload identities, credential providers, and auditability. AWS documentation describes AgentCore Identity as supporting agent identities through workload identities and states that client credentials are used when an agent authenticates directly with each system using its own identity and preconfigured permissions. IAM users with long-lived keys are a weaker security design. Authorization code is user-delegated and interactive, so it does not fit a no-user-interaction autonomous agent workflow.

This explanation is based on AWS documentation and best practices but is paraphrased, not a literal extract.

The scenario describes a central event broker and multiple microservices running in separate AWS accounts that all need to consume the same events. The requirement is to distribute events from a central location to many microservices across accounts in a scalable and loosely coupled way, as part of modernizing to a microservices architecture.

Amazon EventBridge is a serverless event bus service designed for event-driven architectures. It supports centralized event buses, rich content-based filtering with rules, and cross-account event routing. With EventBridge, you can create an event bus in a central account and define rules that match specific event patterns (for example, by microservice or event type). Each rule can have one or more targets, including event buses in other AWS accounts. This supports the pattern of having a central event bus in one account and distributing relevant events to other accounts, where each microservice consumes events either directly from its own event bus or through additional rules and targets in its own account.

In this solution, you create a new EventBridge event bus in the central account and grant the appropriate permissions for cross-account access (option B). You then define EventBridge rules on the central event bus, filtered per microservice or per event category, and configure the rules to send events to the respective event buses or targets in the microservices’ accounts. EventBridge handles the fan-out and delivery of events across accounts in a managed, scalable way, which aligns with the modernization goal and reduces the operational overhead of managing custom routing or polling logic.

Option A uses an SNS topic with SQS queues in each account. This is a valid fan-out pattern and supports cross-account subscriptions, but it is more suited to traditional pub/sub messaging and does not provide the event routing, filtering, and observability features that EventBridge offers for modern event-driven microservices. In scenarios that explicitly mention an event broker and modernization, EventBridge is the recommended service.

Option C is incorrect because Kinesis Data Streams is designed for high-throughput streaming data and requires building and managing consumer applications. The description in the option is also technically inaccurate; Kinesis does not “invoke” microservices directly as event targets in the same way as EventBridge or SNS does. Instead, applications must read from the stream.

Option D uses a single central SQS queue that all microservices read from. SQS provides at-least-once delivery to competing consumers, which means multiple consumers reading from the same queue will typically share messages rather than each getting all messages. This does not satisfy the requirement for multiple microservices to each receive the same events independently. It also reduces decoupling and observability compared to an event bus model.

Therefore, creating an Amazon EventBridge event bus in the central account with rules to distribute events across accounts (option B) best meets the requirements for distributing events from a central broker to multiple microservices across accounts in a modernized architecture.

C: Multipart uploads can leave incomplete parts behind, which incur storage costs. Expiring them after 7 days minimizes waste and saves cost.

E: Since objects are infrequently accessed after 180 days, transitioning toS3 Standard-IAis cost-effective, especially for large files > 128 KB (your 100 MB+ files qualify).

Ais for shifting download cost, not reducing your S3 storage expenses.

Bhelps with upload speed butincreases cost.

Dis too aggressive; Glacier is not suited for access patterns within the first few days.

The application is business-critical with an SLA requirement of 99.95% uptime and is currently limited by an on-premises data center. The architecture includes a PostgreSQL database and separate business logic and presentation layers. Remote users experience high latency when accessing the application, which is latency-sensitive.

To meet or exceed a 99.95% SLA for the database while minimizing operational burden, a managed database service with built-in high availability is preferred. Amazon Aurora PostgreSQL-compatible editions provide high availability and durability by automatically replicating data across multiple Availability Zones in a Region. Aurora can offer higher availability and faster failover compared to self-managed databases on EC2, and offloads patching, backups, and maintenance.

For the application and presentation layers, running them on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer preserves the basic architecture (stateless application servers behind a load balancer) while moving to a managed, highly available environment that can scale out and in automatically based on demand. This results in minimal changes to the application components and provides improved resilience compared to on-premises VMs.

Remote users experience slow load times because their desktop clients connect over higher-latency networks to the on-premises servers. By using Amazon AppStream 2.0, the application can be streamed from AWS to end users. AppStream 2.0 runs the desktop application close to the backend database and application servers within AWS, and streams only the display and input over the network. This significantly reduces the effect of latency between the user and the application backend, improving user experience, especially for latency-sensitive desktop applications, without requiring major changes to the application itself.

Option A uses a PostgreSQL database on EC2, which requires the customer to manage availability, backups, patching, and failover. This does not provide the same managed high-availability guarantees as Aurora or RDS Multi-AZ. It also suggests allocating a full Amazon WorkSpaces desktop per user, which can be more expensive and more complex than streaming only the application via AppStream 2.0.

Option C uses Amazon RDS for PostgreSQL with Multi-AZ configuration, which provides high availability and is a valid managed option. However, it runs the application and presentation layers on Fargate containers behind a Network Load Balancer. NLB is typically used for TCP-level load balancing and is better suited for protocols that require low-level handling, whereas HTTP/HTTPS web applications are commonly placed behind an Application Load Balancer, which provides advanced HTTP routing features. Also, ElastiCache can reduce database load and improve response times but does not directly solve the end-user network latency issue in a desktop client scenario.

Option D is incorrect because Amazon Redshift is a data warehouse service, not an operational PostgreSQL-compatible database for transaction processing. It is not appropriate for hosting the application’s primary relational database. CloudFront accelerates delivery of static and cached web content, not interactive desktop client-server traffic.

Therefore, migrating the database to Aurora PostgreSQL, running the application and presentation layers on EC2 Auto Scaling behind an Application Load Balancer, and using Amazon AppStream 2.0 to deliver the application to remote users (option B) meets the availability requirements, improves user experience, requires relatively little change to the application architecture, and minimizes operational costs compared to more complex or less-managed alternatives.

Transit GW + Direct Connect GW + Transit VIF + enabled SiteLink if two different DX locationshttps://aws.amazon.com/blogs/networking-and-content-delivery/introducing-aws-direct-connect-sitelink/

Configuring AWS CloudTrail to log S3 data events will enable logging all activities for objects in the S3 bucket1. Data events are object-level API operations such as GetObject, DeleteObject, and PutObject1. Configuring Amazon S3 to send object deletion events to an Amazon EventBridge event bus that publishes to an Amazon Simple Notification Service (Amazon SNS) topic will enable sending email notifications every time there is an attempt to delete data in the S3 bucket2. EventBridge can route events from S3 to SNS, which can send emails to subscribers2. Configuring a new S3 bucket to store the logs with an S3 Lifecycle policy will enable keeping the logs for 5 years in a cost-effective way3. A lifecycle policy can transition the logs to a cheaper storage class such as Glacier or delete them after a specified period of time3.

The best solution is to provision a new Amazon Elastic File System (Amazon EFS) file system that uses Max I/O performance mode and mount the EFS file system on each EC2 instance in the cluster. Amazon EFS is a fully managed, scalable, and elastic file storage service that supports the POSIX standard and can be accessed by multiple EC2 instances concurrently. Amazon EFS offers two performance modes: General Purpose and Max I/O. Max I/O mode is designed for highly parallelized workloads that can tolerate higher latencies than the General Purpose mode. Max I/O mode provides higher levels of aggregate throughput and operations per second, which are suitable for big data analytics applications. This solution meets all the requirements of the company. References: Amazon EFS Documentation, Amazon EFS performance modes

Create VPC Endpoint Service:

In the shared VPC, create a VPC endpoint service using the Network Load Balancer (NLB) that fronts the centralized application.

Enable the option to require endpoint acceptance to control which business unit VPCs can connect to the service.

Set Up VPC Endpoints in Business Unit VPCs:

In each business unit VPC, create a VPC endpoint that points to the VPC endpoint service created in the shared VPC.

Use the service name of the endpoint service created in the shared VPC for configuration.

Accept Endpoint Requests:

From the VPC endpoint service console in the shared VPC, review and accept endpoint connection requests from authorized business unit VPCs. This ensures that only authorized VPCs can access the centralized application.

Configure Routing:

Update the route tables in each business unit VPC to direct traffic destined for the centralized application through the VPC endpoint.

This solution ensures secure, private connectivity between the business unit VPCs and the shared VPC, even if there are overlapping CIDR blocks. It leverages AWS PrivateLink and VPC endpoints to provide scalable and controlled access​(AWS Documentation)​​(Amazon Web Services, Inc.)​.

AWS IoT Core’spre-provisioning hookallows custom logic (like serial number validation)beforea device is registered. This ensures that only installed devices connect. Lambda is ideal for such logic.

AWS IoT Just-in-Time Provisioning

https://d1.awsstatic.com/architecture-diagrams/ArchitectureDiagrams/NAT-gateway-centralized-egress-ra.pdf?did=wp_cardandtrk=wp_card

Option B is correct because CloudFormation Git sync is built specifically to connect CloudFormation stacks to source-controlled templates and deployment files in Git repositories. It supports GitHub Enterprise as a provider, can monitor repository changes, and can update stacks after approved changes are merged. AWS documentation states that Git sync works with pull requests and can post comments explaining what changes will be made before a stack update. This directly satisfies the requirement for automatic change set visibility on pull requests without building custom CodePipeline logic or manually processing templates. Option A introduces a custom pipeline, which the question explicitly rejects. Drift detection compares deployed resources against expected configuration; it does not create pull-request change sets. Lambda Hooks evaluate resources during CloudFormation operations but do not provide native Git pull-request synchronization.

https://docs.aws.amazon.com/codedeploy/latest/userguide/integrations-aws-auto-scaling.html

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_org_support-all-features.html

https://docs.aws.amazon.com/singlesignon/latest/userguide/get-started-prereqs-considerations.html

The CloudFront managed prefix list contains the IP ranges for all CloudFront edge locations. By updating the ALB security group ingress to allow access only from this prefix list, the web application will be accessible only by using the CloudFront endpoint. This solution requires the least amount of effort compared to the other options, which involve creating new resources or updating existing ones. This solution also avoids hard-coding IP addresses, which can change over time.

https://docs.aws.amazon.com/global-accelerator/latest/dg/about-accelerators.alb-accelerator.html Option A is wrong. AWS WAF does not support associating with NLB. https://docs.aws.amazon.com/waf/latest/developerguide/waf-chapter.html Option B is wrong. An ALB does not support an Elastic IP address.https://aws.amazon.com/elasticloadbalancing/features/

Creating a quota request template in the us-east-1 Region of the management account ensures it applies to all newly provisioned accounts in the organization.

By specifying the required ml.p2.xlarge training job and endpoint usage quotas in ap-southeast-2, the accounts will automatically receive these higher quotas upon creation, supporting the student lab environments without additional manual quota requests.

This process integrates seamlessly with AWS Organizations for automated and standardized account provisioning.

This option allows the company to use Amazon CloudFront to improve the latency and availability of the API requests by caching the responses at the edge locations closest to the clients1. By enabling caching in the production stage, the company can reduce the number of calls made to the backend services, such as Lambda functions and Aurora Serverless DB cluster, and save on costs and resources2. This option also helps to handle traffic spikes and reduce database memory errors by serving cached responses instead of querying the database repeatedly.

Choosing an API endpoint type

Enabling API caching to enhance responsiveness

IAM Access Analyzer helps you identify the resources in your organization and accounts, such as Amazon S3 buckets or IAM roles, shared with an external entity. This lets you identify unintended access to your resources and data, which is a security risk. IAM Access Analyzer identifies resources shared with external principals by using logic-based reasoning to analyze the resource-based policies in your AWS environment.https://docs.aws.amazon.com/IAM/latest/UserGuide/what-is-access-analyzer.html

https://aws.amazon.com/premiumsupport/knowledge-center/s3-access-denied-error-kms/

" An error occurred (AccessDenied) when calling the PutObject operation: Access Denied " This error message indicates that your IAM user or role needs permission for the kms:GenerateDataKey action.

Mountpoint for Amazon S3allows EC2 instances to directly access files in S3 as aPOSIX-compliant mount point, ensuring they always get the latest data without copying or syncing.

It’s simple and cost-effective for read-heavy patterns.

Mountpoint for Amazon S3

https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/custom-tags.htmlhttps://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/configurecostallocreport.html

A is correct because:

App2Containersupports packaging .NET Framework apps into containers without porting to .NET Core.

ECS with EC2gives full control over the OS and patching.

ALBhandles microservice-level load balancing.

RDS for SQL Serverwith Multi-AZ ensures high availability.

Options B, C, and D involve Aurora MySQL (incompatible with SQL Server features) orrequire .NET Core, which involves more significant application changes.

https://docs.aws.amazon.com/config/latest/developerguide/config-rule-multi-account-deployment.html

https://docs.aws.amazon.com/config/latest/developerguide/aggregate-data.html

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps_examples_tagging.html

Step B is necessary so that the user in the destination account has the necessary permissions to access the source bucket and list its contents, read its objects. Step D is needed so that the user in the destination account has the necessary permissions to access the destination bucket and list contents, put objects, and set object ACLs Step F is necessary because the aws s3 sync command needs to be run using the IAM user credentials from the destination account, so that the objects will have the appropriate permissions for the user in the destination account once they are copied.

The data scientists’ EC2 instances in a separate account must access S3 data in a controlled way that satisfies the policy: only authorized networks may access the IoT data. “Authorized networks” in this context means traffic must originate from approved VPCs and must not traverse the public internet.

First, traffic from the EC2 instances to S3 must stay on the AWS network without using public endpoints. A gateway VPC endpoint for S3 in the data scientists’ VPC (option A) allows EC2 instances in that VPC to reach S3 over private connectivity. When using a gateway VPC endpoint, the route tables of the subnets associated with the endpoint automatically route S3 traffic through the endpoint. This ensures that access to S3 is from an authorized network (the VPC) instead of from the public internet.

Second, access must be constrained such that only calls made through the intended S3 access mechanism are allowed. The data lake bucket already has an S3 access point. S3 access points can be restricted by policy and can be referenced in bucket policies through the s3:DataAccessPointArn condition key. By using an S3 bucket policy that allows s3:GetObject only when s3:DataAccessPointArn matches the expected access point ARN (option E), the company can enforce that all valid access uses the approved access point configuration. Combined with the access point’s own network configuration and the VPC endpoint, access is limited to authorized networks.

Option B, blocking public access on the access point, is a good general security practice but does not by itself guarantee that access is restricted to authorized VPC networks. The main enforcement must be through VPC endpoints and bucket/access point policies.

Option C denies s3:GetObject when s3:DataAccessPointArn is a valid access point ARN, which is the opposite of what is required. This would prevent intended access via the access point rather than allow it.

Option D is not correct because gateway VPC endpoints for S3 are configured via endpoint associations with route tables, not by directly routing to an access point in the route table. Traffic to S3 is routed to the VPC endpoint, and the endpoint plus S3 policies determine access.

Therefore, creating a gateway VPC endpoint for S3 in the data scientists’ VPC (option A) and using a bucket policy condition on s3:DataAccessPointArn to allow access only through the authorized access point (option E) together meet the requirement of secure, network-restricted access from authorized networks.

Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use Amazon Textract and Amazon Comprehend to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system ' s API. This solution meets the requirements of accurate form extraction, minimal time to market, and minimal long-term operational overhead. Amazon Textract and Amazon Comprehend are fully managed and serverless services that can perform OCR and extract relevant data from the forms, which eliminates the need to develop custom libraries or train and host models. Using AWS Step Functions and Lambda allows for easy automation of the process and the ability to scale as needed.

A. In the production account, create a new IAM policy that allows read and write access to the S3 bucket. The policy grants the necessary permissions to access the assets in the production S3 bucket.

C. In the production account, create a role. Attach the new policy to the role. Define the development account as a trusted entity. By creating a role and attaching the policy, and then defining the development account as a trusted entity, the development account can assume the role and access the production S3 bucket with the read and write permissions.

E. In the development account, create a group that contains all the IAM users of the design team. Attach a different IAM policy to the group to allow the sts:AssumeRole action on the role in the production account. The IAM policy attached to the group allows the design team members to assume the role created in the production account, thereby giving them access to the production S3 bucket.

Step 1: Create a role in the Production Account; create the role in the Production account and specify the Development account as a trusted entity. You also limit the role permissions to only read and write access to the productionapp bucket. Anyone granted permission to use the role can read and write to the productionapp bucket. Step 2: Grant access to the role Sign in as an administrator in the Development account and allow the AssumeRole action on the UpdateApp role in the Production account. So, recap, production account you create the policy for S3, and you set development account as a trusted entity. Then on the development account you allow the sts:assumeRole action on the role in production account.https://docs.aws.amazon.com/IAM/latest/UserGuide/tutorial_cross-account-with-roles.html

To migrate an on-premises SFTP site to AWS with high availability and a set of static public IP addresses for external vendors, the best solution is to create an AWS Transfer Family server with an internet-facing VPC endpoint. Assigning Elastic IP addresses to each subnet and configuring the server to store files in an Amazon Elastic File System (EFS) that spans multiple Availability Zones ensures high availability and consistent access. This approach minimizes operational overhead by leveraging AWS managed services and eliminates the need to manage underlying infrastructure.

AWS Documentation on AWS Transfer Family and Amazon Elastic File System provides detailed instructions on setting up a highly available SFTP environment on AWS. This solution is in line with AWS best practices for migrating and modernizing applications with minimal disruption and ensuring high availability and security.

D is correct because this solution modernizes the application into aserverless architectureusing API Gateway and Lambda for scalable microservices.Aurora Serverless v2supports SQL workloads and auto-scales based on demand.AWS SCTallows migration of SQL Server stored procedures to Aurora-compatible formats. This setup ensures cost efficiency, scalability, and minimal manual intervention.

A rehosts but doesn’t refactor into microservices.

B uses MySQL, which might not support the SQL Server-specific stored procedures fully.

C adds unnecessary complexity and loses relational database functionality by using DAX.

https://aws.amazon.com/blogs/architecture/accelerating-your-migration-to-aws/ Build a business case with AWS Migration Evaluator The foundation for a successful migration starts with a defined business objective (for example, growth or new offerings). In order to enable the business drivers, the established business case must then be aligned to a technical capability (increased security and elasticity). AWS Migration Evaluator (formerly known as TSO Logic) can help you meet these objectives. To get started, you can choose to upload exports from third-party tools such as Configuration Management Database (CMDB) or install a collector agent to monitor. You will receive an assessment after data collection, which includes a projected cost estimate and savings of running your on-premises workloads in the AWS Cloud. This estimate will provide a summary of the projected costs to re-host on AWS based on usage patterns. It will show the breakdown of costs by infrastructure and software licenses. With this information, you can make the business case and plan next steps.

Create a Transit Gateway:

In the shared services account, create a new AWS Transit Gateway. This serves as a central hub to connect multiple VPCs, simplifying the network topology and management.

Configure Transit Gateway Attachments:

Attach the VPC containing the Aurora DB cluster to the transit gateway. This allows the shared services VPC to communicate through the transit gateway.

Create Resource Share with AWS RAM:

Use AWS Resource Access Manager (AWS RAM) to create a resource share for the transit gateway. Share this resource with all development accounts. AWS RAM allows you to securely share your AWS resources across AWS accounts without needing to duplicate them.

Accept Resource Shares in Development Accounts:

Instruct each development team to log into their respective AWS accounts and accept the transit gateway resource share. This step is crucial for enabling cross-account access to the shared transit gateway.

Configure VPC Attachments in Development Accounts:

Each development account needs to attach their VPC to the shared transit gateway. This allows their VPCs to route traffic through the transit gateway to the Aurora DB cluster in the shared services account.

Update Route Tables:

Update the route tables in each VPC to direct traffic intended for the Aurora DB cluster through the transit gateway. This ensures that network traffic is properly routed between the development VPCs and the shared services VPC.

Using a transit gateway simplifies the network management and reduces operational overhead by providing a scalable and efficient way to interconnect multiple VPCs across different AWS accounts.

References

AWS Database Blog on RDS Proxy for Cross-Account Access【48】.

AWS Architecture Blog on Cross-Account and Cross-Region Aurora Setup【49】.

DEV Community on Managing Multiple AWS Accounts with Organizations【51】.

Amazon Elastic Container Service (ECS) on AWS Fargate is a fully managed service that allows you to run containers without having to manage the underlying infrastructure. When you launch tasks on Fargate, resources are automatically allocated based on the number of tasks running, which reduces the operational overhead.

Using ECS on Fargate allows you to assign custom tags to tasks using the --tags option in the run-task command, as described in the documentation:https://docs.aws.amazon.com/cli/latest/reference/ecs/run-task.htmlYou can also set enableECSManagedTags to true, which allows the service to automatically add the cluster name and service name as tags.https://docs.aws.amazon.com/AmazonECS/latest/developerguide/task-placement-constraints.html#tag-based-scheduling

https://docs.aws.amazon.com/AmazonS3/latest/userguide/replication-time-control.html

Creating an Amazon EventBridge rule that runs every business day in the evening to stop instances and another rule that runs every business day in the morning to start instances based on the tag will reduce costs with the least operational effort. This approach allows for instances to be stopped during non-business hours when they are not in use, reducing the costs associated with running them. It also allows for instances to be started again in the morning when the development and testing teams need to use them.

https://docs.aws.amazon.com/firehose/latest/dev/creating-the-stream-to-splunk.html

This option uses different types of savings plans and reserved nodes to minimize the company’s overall monthly costs for running its application on EC2 instances, Lambda functions, and MemoryDB cache nodes. Savings plans are flexible pricing models that offer significant savings on AWS usage (up to 72%) in exchange for a commitment of a consistent amount of usage (measured in $/hour) for a one-year or three-year term. There are two types of savings plans: Compute Savings Plans and EC2 Instance Savings Plans. Compute Savings Plans apply to any compute usage across EC2 instances, Fargate containers, Lambda functions, SageMaker notebooks, and ECS tasks. EC2 Instance Savings Plans apply to a specific instance family within a region and provide more savings than Compute Savings Plans (up to 66% versus up to 54%). Reserved nodes are similar to savings plans but apply only to MemoryDB cache nodes. They offer up to 55% savings compared to on-demand pricing.

https://docs.aws.amazon.com/fsx/latest/WindowsGuide/what-is.htmlhttps://docs.aws.amazon.com/directoryservice/latest/admin-guide/ms_ad_join_instance.html

Option A is correct because using an Elastic Load Balancer and an Auto Scaling group with a minimum capacity of two instances can improve the availability and scalability of the EC2 instances that host the application. The load balancer can distribute traffic across multiple instances and the Auto Scaling group can replace any unhealthy instances automatically1

Option D is correct because modifying the DB instance to create a Multi-AZ deployment that extends across two Availability Zones can improve the availability and durability of the RDS for MariaDB database. Multi-AZ deployments provide enhanceddata protection and minimize downtime by automatically failing over to astandby replica in another Availability Zone in case of a planned or unplanned outage4

Option F is correct because creating a replication group for the ElastiCache for Redis cluster and enabling Multi-AZ on the cluster can improve the availability and fault tolerance of the in-memory data store. A replication group consists of a primary node and up to five read-only replica nodes that are synchronized with the primary node using asynchronous replication. Multi-AZ allows automatic failover to one of the replicas if the primary node fails or becomes unreachable6

The most effortless way to ensure that all new Amazon Elastic Block Store (EBS) volumes are encrypted at rest is to enable EBS encryption by default in all AWS Regions. This setting automatically encrypts all new EBS volumes and snapshots created in the account, thereby ensuring compliance with encryption policies without the need for manual intervention or additional monitoring.

AWS Documentation on Amazon EBS encryption provides guidance on enabling EBS encryption by default. This approach aligns with AWS best practices for data protection and compliance, ensuring that all new EBS volumes adhere to encryption requirements with minimal operational effort.

They all mention using spot instances and EKS based on EC2. A spot instance is not appropriate for a production server and the company is developing new application designed for AWS Fargate, which means we must plan the future cost improvement including AWS Fargate. https://aws.amazon.com/savingsplans/compute-pricing/

Comprehensive and Detailed Explanation:

Option C offers a scalable and low-overhead solution for managing custom controls across multiple AWS accounts:

AWS Control Tower provides a pre-configured environment to set up and govern a secure, multi-account AWS environment based on AWS best practices.

Customizations for AWS Control Tower (CfCT) allows for the deployment of custom configurations and resources, such as AWS Config rules and IAM policies, across accounts and organizational units using AWS CloudFormation templates.

Amazon EventBridge integrates with AWS Control Tower to automate the deployment of customizations during account provisioning events, ensuring that all new accounts adhere to the defined controls without manual intervention.

This approach ensures consistent enforcement of custom controls across all accounts with minimal operational overhead.

The company is building an active-active, multi-Region architecture for an ECS-based web application behind ALBs, with shared container images in ECR and a relational database in Aurora MySQL. The goal is to duplicate the setup in a second Region with minimal operational overhead while allowing minor replication latency.

A is required because ACM certificates are Regional for use with Regional resources like an Application Load Balancer. An ALB in the secondary Region needs an ACM certificate that exists in that same Region to terminate HTTPS. Requesting (or importing) a certificate in the secondary Region and attaching it to the ALB HTTPS listener is the standard managed approach with low operational overhead.

B is not correct because ACM certificates are not shared “across Regions” for direct use by a Regional ALB in another Region. Sharing via AWS RAM is used for sharing resources across accounts within a Region (and for supported resource types), but it does not provide a mechanism to use an ACM certificate issued in one Region directly on an ALB in a different Region.

D is required because the application runs in both Regions and must be able to pull container images locally with minimal operational work. Amazon ECR supports cross-Region replication so images pushed in the primary Region can be replicated automatically to the secondary Region’s ECR repositories. This avoids building custom copy pipelines and reduces the chance of drift. Once replication is enabled, updating images in the primary Region can automatically populate the secondary Region repository.

C is not the best fit. A VPC endpoint for ECR is about private connectivity to ECR within a Region. It does not solve the multi-Region image availability problem by itself, and it introduces the idea of pulling images from the primary Region, which is unnecessary and less optimal when cross-Region replication can keep images local to each Region.

E is required because the database must support multi-Region application operation with tolerable replication latency. Aurora Global Database is the managed feature designed to replicate an Aurora cluster to a secondary Region with low-latency replication and minimal operational overhead compared to custom replication. Enabling write forwarding allows applications in the secondary Region to send write requests to the secondary cluster endpoint, with Aurora forwarding those writes to the primary writer. This reduces application changes compared to forcing all writes to go directly to the primary Region endpoints, and it supports an active-active application deployment pattern where both Regions can accept traffic.

F is not correct because “cross-Region writes between DB clusters” and “multiple writer instances in the primary Region” do not provide a managed, multi-Region, multi-writer Aurora design as described. Aurora’s managed multi-Region pattern is Aurora Global Database with a single primary writer in one Region (with the option to promote a secondary Region during failover) and optional write forwarding from a secondary Region to the primary. The option’s described cross-Region multi-writer behavior is not a standard supported Aurora configuration and would not be the least operational overhead approach.

Therefore, the least operational overhead combination that correctly addresses HTTPS termination in the secondary Region, image availability in both Regions, and managed multi-Region database replication is A, D, and E.

The company should use AWS Amplify to create a static website for uploads of media files. The company should use Amplify Hosting to serve the website through Amazon CloudFront. The company should use Amazon S3 to store the uploaded media files. The company should use Amazon Cognito to authenticate users. This solution will meet the requirements with the least operational overhead because AWS Amplify is a complete solution that lets frontend web and mobile developers easily build, ship, and host full-stack applications on AWS, with the flexibility to leverage the breadth of AWS services as use cases evolve. No cloud expertise needed1. By using AWS Amplify, the company can refactor the application to a serverless architecture that reduces operational complexity and costs. AWS Amplify offers the following features and benefits:

Amplify Studio: A visual interface that enables you to build and deploy a full-stack app quickly, including frontend UI and backend.

Amplify CLI: A local toolchain that enables you to configure and manage an app backend with just a few commands.

Amplify Libraries: Open-source client libraries that enable you to build cloud-powered mobile and web apps.

Amplify UI Components: Open-source design system with cloud-connected components for building feature-rich apps fast.

Amplify Hosting: Fully managed CI/CD and hosting for fast, secure, and reliable static and server-side rendered apps.

By using AWS Amplify to create a static website for uploads of media files, the company can leverage Amplify Studio to visually build a pixel-perfect UI and connect it to a cloud backend in clicks. By using Amplify Hosting to serve the website through Amazon CloudFront, the company can easily deploy its web app or website to the fast, secure, and reliable AWS content delivery network (CDN), with hundreds of points of presence globally. Byusing Amazon S3 to store the uploaded media files, the company can benefit from a highly scalable, durable, and cost-effective object storage service that can handle any amount of data2. By using Amazon Cognito to authenticate users, the company can add user sign-up, sign-in, and access control to its web app with a fully managed service that scales to support millions of users3.

The other options are not correct because:

Using AWS Application Migration Service to migrate the application server to Amazon EC2 instances would not refactor the application or accelerate development. AWS Application Migration Service (AWS MGN) is a service that enables you to migrate physical servers, virtual machines (VMs), or cloud servers from any source infrastructure to AWS without requiring agents or specialized tools. However, this would not address the challenges of overutilization and data uploads failures. It would also not reduce operational overhead or costs compared to a serverless architecture.

Creating a static website for uploads of media files and using AWS AppSync to create an API would not be as simple or fast as using AWS Amplify. AWS AppSync is a service that enables you to create flexible APIs for securely accessing, manipulating, and combining data from one or more data sources. However, this would require more configuration and management than using Amplify Studio and Amplify Hosting. It would also not provide authentication features like Amazon Cognito.

Setting up AWS IAM Identity Center (AWS Single Sign-On) to give users the ability to sign in to the application would not be as suitable as using Amazon Cognito. AWS Single Sign-On (AWS SSO) is a service that enables you to centrally manage SSO access and user permissions across multiple AWS accounts and business applications. However, this service is designed for enterprise customers who need to manage access for employees or partners across multiple resources. It is not intended for authenticating end users of web or mobile apps.

https://repost.aws/knowledge-center/kinesis-readprovisionedthroughputexceeded

Follow Data Streams best practices

To mitigate ReadProvisionedThroughputExceeded exceptions, apply these best practices:

• Reshard your stream to increase the number of shards in the stream.

• Use consumers with enhanced fan-out. For more information about enhanced fan-out, see Developing custom consumers with dedicated throughput (enhanced fan-out).

• Use an error retry and exponential backoff mechanism in the consumer logic if ReadProvisionedThroughputExceeded exceptions are encountered. For consumer applications that use an AWS SDK, the requests are retried by default.

By reducing the number of data nodes in the cluster to 2 and adding UltraWarm nodes to handle the expected capacity, the company can reduce the cost of running the cluster. Additionally, configuring the indexes to transition to UltraWarm when OpenSearch Service ingests the data will ensure that the data is stored in the most cost-effective manner. Finally, transitioning the input data to S3 Glacier Deep Archive after 1 month by using an S3 Lifecycle policy will ensure that the data is retained for compliance purposes, while also reducing the ongoing costs.

Gateway VPC Endpoint for S3:

Create a gateway VPC endpoint for Amazon S3 in your VPC. This allows instances in your VPC to communicate with Amazon S3 without going through the internet, reducing data transfer costs and improving security.

Add Spot Instances to ECS Cluster:

Add another ECS capacity provider that uses an Auto Scaling group of Spot Instances. Configure this new capacity provider to share the load with the existing On-Demand Instances by setting an appropriate weight in the capacity provider strategy. Spot Instances offer significant cost savings compared to On-Demand Instances.

Configure Capacity Provider Strategy:

Adjust the ECS service ' s capacity provider strategy to utilize both On-Demand and Spot Instances effectively. This ensures a balanced distribution of tasks across both instance types, optimizing cost while maintaining availability.

By implementing a gateway VPC endpoint for S3 and incorporating Spot Instances into the ECS cluster, the company can significantly reduce operational costs without compromising on the availability or performance of the platform.

References

AWS Cost Optimization Blog on VPC Endpoints

AWS ECS Documentation on Capacity Providers

Create a Transit Gateway:

Step 1: In the AWS Management Console, navigate to the VPC Dashboard.

Step 2: Select " Transit Gateways " and click on " Create Transit Gateway " .

Step 3: Configure the transit gateway by providing a name and setting the options for Amazon side ASN and VPN ECMP support as needed.

Step 4: Attach each of the company’s VPCs and relevant subnets to the transit gateway. This centralizes the network management and simplifies the routing configurations, supporting scalable and flexible network architecture.

Set Up AWS PrivateLink:

Step 1: Create a Network Load Balancer (NLB) in the management VPC that points to the compute resource responsible for license validation.

Step 2: Create an AWS PrivateLink endpoint service pointing to this NLB.

Step 3: Allow each customer ' s VPC to create an interface endpoint to this PrivateLink service. This setup enables secure and private communication between the customer VPCs and the management VPC, ensuring one-way access from each customer ' s VPC to the management VPC for license validation.

This combination leverages the benefits of AWS Transit Gateway for scalable and centralized routing, and AWS PrivateLink for secure and private service access, meeting the requirement with minimal operational overhead.

References

Amazon VPC-to-Amazon VPC Connectivity Options

AWS PrivateLink - Building a Scalable and Secure Multi-VPC AWS Network Infrastructure

Connecting Your VPC to Other VPCs and Networks Using a Transit Gateway

 AWS Trusted Advisor is a service that provides real-time guidance to help users provision their resources following AWS best practices1. One of the Trusted Advisor checks is “Low Utilization Amazon EC2 Instances”, which identifies EC2 instances that appear to be underutilized based on CPU, network I/O, and disk I/O metrics1. This check can help users optimize the cost and size of their EC2 instances by recommending smaller or more appropriate instance types.

AWS Compute Optimizer is a service that analyzes the configuration and utilization metrics of AWS resources and generates optimization recommendations to reduce the costand improve the performance of workloads2. Compute Optimizer supports four types of AWS resources: EC2 instances, EBS volumes, ECS services on AWSFargate, and Lambdafunctions2. For EC2 instances, Compute Optimizer evaluates the vCPUs, memory, storage, and other specifications, as well as the CPU utilization, network in and out, disk read and write, and other utilization metrics of currently running instances3. It then recommends optimal instance types based on price-performance trade-offs.

Option A is incorrect because purchasing AWS Business Support or AWS Enterprise Support for the account will not directly help with cost-optimization and sizing of EC2 instances. However, these support plans do provide access to more Trusted Advisor checks than the basic support plan1.

Option C is incorrect because installing the Amazon CloudWatch agent and configuring memory metric collection on the EC2 instances will not provide any optimization recommendations by itself. However, memory metrics can be used by Compute Optimizer to enhance its recommendations if enabled3.

Option E is incorrect because creating an EC2 Instance Savings Plan for the AWS Regions, instance families, and operating systems of interest will not help with cost-optimization and sizing of EC2 instances. Savings Plans are a flexible pricing model that offer lower prices on Amazon EC2 usage in exchange for a commitment to a consistent amount of usage for a 1- or 3-year term4. Savings Plans do not affect the configuration or utilization of EC2 instances.

You can use a Glue crawler to populate the AWS Glue Data Catalog with tables. The Lambda function can be triggered using S3 event notifications when object create events occur. The Lambda function will then trigger the Glue ETL job to transform the records masking the sensitive data and modifying the output format to JSON. This solution meets all requirements.

A is correct becauseSSM Agent must be installed and actively runningon the instance for it to be managed by Systems Manager. Imported VMs typically do not have the agent installed by default.

B is also correct becausean IAM instance profilewith at least the AmazonSSMManagedInstanceCore policy must be attached to the instance to allow communication with SSM.

Option C is unnecessary if the instance has internet access (via public IP).

Option D relates to migration assessments, not Systems Manager.

Option E is good practice but not required for the instance to appear as managed.

https://aws.amazon.com/blogs/database/amazon-dynamodb-auto-scaling-performance-and-cost-optimization-at-any-scale/ " As you can see, there are compelling reasons to use DynamoDB auto scaling with actively changing traffic. Auto scaling responds quickly and simplifies capacity management, which lowers costs by scaling your table’s provisioned capacity and reducing operational overhead. "

Generally, unstructured data should be converted structured data before querying them. AWS Glue can do that. https://docs.aws.amazon.com/glue/latest/dg/schema-relationalize.html https://docs.aws.amazon.com/athena/latest/ug/glue-athena.html

For a development environment that requires frequent resource recreation and connectivity to applications hosted in a shared services account, the most efficient solution involves using AWS Resource Access Manager (RAM) and the transit gateway in the shared services account. By turning on automatic acceptance for the transit gateway in the shared services account and sharing it with the development account through AWS RAM, the development team can easily recreate their connection as needed without manual intervention. This setup allows for scalable, flexible connectivity between accounts while minimizing operational overhead and ensuring consistent access to shared services.

AWS Documentation on AWS Resource Access Manager and Transit Gateway provides guidance on sharing network resources across AWS accounts and enabling automatic acceptance for transit gateway attachments. This approach is also supported by AWS best practices for multi-account strategies using AWS Organizations and network architecture.

The compute layer uses AWS Fargate, so the correct savings instrument is a Compute Savings Plan, not an EC2 Instance Savings Plan. AWS states that Compute Savings Plans automatically apply to EC2, Fargate, and Lambda usage, while EC2 Instance Savings Plans are restricted to EC2 instance usage in a selected family and Region. The database layer is Aurora MySQL, so a Reserved DB Instance is appropriate only for the predictable production database, not for development and testing databases that are regularly shut down. A 1-year commitment also matches the stated one-year predictable production demand. Buying 3-year commitments for all accounts would overcommit spend, especially for non-production environments with intermittent usage. Therefore, centralized 1-year production database reservations plus a 1-year Compute Savings Plan is the most cost-effective match.

Identify Proxy EC2 Instances:

Determine which EC2 instances in the private subnets are running the proxy server software.

Disable Source/Destination Checks:

For each of these EC2 instances, go to the AWS Management Console.

Navigate to the EC2 dashboard, select the instance, and choose " Actions " > " Networking " > " Change Source/Dest.Check " .

Disable the source/destination check for these instances.

Disabling source/destination checks allows the EC2 instances to route traffic appropriately, enabling them to function as network appliances or proxies. This ensures that traffic from other instances in the private subnets can be routed through the proxy instances to the internet, meeting the company ' s security requirements.

References

Amazon EC2 User Guide on Source/Destination Checks

In this solution, the company can use Amazon SES to send email messages, which will minimize operational overhead as SES is a fully managed service that handles sending and receiving email messages. The company can store the email template on Amazon SES with parameters for the customer data and use an AWS Lambda function to call the SendTemplatedEmail API operation, passing in the customer data to replace the parameters and the email destination. This solution eliminates the need to set up and manage an SMTP server on EC2 instances, which can be costly and time-consuming.

Amazon Simple Notification Service (Amazon SNS) is a fully managed pub/sub messaging service that enables users to send messages to multiple subscribers1. Users can sendevent details to an Amazon SNS topic and configure an AWS Lambda function as a subscriber to the SNS topic to process the events. Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources2. Users can add an on-failure destination to the function and set an Amazon Simple Queue Service (Amazon SQS) queue as the target. Amazon SQS is a fully managed message queuing service that enables users to decouple and scale microservices, distributed systems, and serverless applications3. This way, if a processing error occurs, the event will move into the separate queue for review.

Option B is incorrect because publishing events to an Amazon SQS queue and creating an Amazon EC2 Auto Scaling group will not have the ability to scale in and out based on the number of events that the solution receives. Amazon EC2 is a web service that provides secure, resizable compute capacity in the cloud. Auto Scaling is a feature that helps users maintain application availability and allows them to scale their EC2 capacity up or down automatically according to conditions they define. However, for this use case, using SQS and EC2 will not take advantage of the serverless capabilities of Lambda and SNS.

Option C is incorrect because writing events to an Amazon DynamoDB table and configuring a DynamoDB stream for the table will not have the ability to move events into a separate queue for review if a processing error occurs. Amazon DynamoDB is a fully managed key-value and document database that delivers single-digit millisecond performance at any scale. DynamoDB Streams is a feature that captures data modification events in DynamoDB tables. Users can configure the stream to invoke a Lambda function, but they cannot configure an on-failure destination for the function.

Option D is incorrect because publishing events to an Amazon EventBridge event bus and setting an Application Load Balancer (ALB) as the event bus target will not have the ability to move events into a separate queue for review if a processing error occurs. Amazon EventBridge is a serverless event bus service that makes it easy to connect applications with data from a variety of sources. An ALB is a load balancer that distributes incoming application traffic across multiple targets, such as EC2 instances, containers, IP addresses, Lambda functions, and virtual appliances. Users can configure EventBridge to retry events, but they cannot configure an on-failure destination for the ALB.

Using AWS Transfer Family with Amazon S3 storage eliminates the need to manage file servers and offers a scalable and durable file storage solution.

S3 event notifications automatically trigger the AWS Glue transformation jobs upon file arrival, eliminating manual job scheduling.

AWS Glue jobs can be configured to send a notification to an Amazon SQS queue after completion, maintaining the messaging workflow with minimal operational overhead.

This fully managed approach simplifies the architecture and leverages AWS-native automation.

This explanation is based on AWS documentation and best practices but is paraphrased, not a literal extract.

The company needs two things:

To serve static content from the AWS Region geographically closest to each customer.

To send data from on premises to Amazon S3 with minimal latency and without traversing the public internet, while keeping operational overhead low.

S3 Multi-Region Access Points provide a single global endpoint that fronts multiple S3 buckets in different Regions. Multi-Region Access Points use latency-based routing and failover capabilities to direct client requests automatically to the closest healthy Regional S3 bucket that is part of the Multi-Region Access Point. This directly addresses the requirement to serve content from the closest Region with high performance and reliability, and it avoids the need to build and maintain custom routing logic.

Behind the scenes, S3 Multi-Region Access Points rely on S3 Replication between the Regional buckets that participate in the Multi-Region Access Point. When you configure a Multi-Region Access Point, you set up the participating buckets and replication configuration once; AWS then manages routing and failover, reducing operational overhead compared to manually configuring and managing multiple S3 endpoints and application logic.

For the on-premises environment, the company wants to send data to S3 with minimal latency and without using the public internet. AWS Direct Connect provides a dedicated network connection from the on-premises network into AWS, avoiding the public internet and generally providing lower and more consistent latency. AWS PrivateLink allows private connectivity to supported AWS services (such as S3 Multi-Region Access Points via interface endpoints) from within a VPC using private IP addresses. When you combine Direct Connect with PrivateLink, traffic flows from on-premises over a private dedicated connection into a VPC and then privately to the Multi-Region Access Point through an interface VPC endpoint, satisfying the requirement for no public internet exposure.

Therefore, implementing S3 Multi-Region Access Points (option A) addresses the global content serving and Regional routing, and using AWS PrivateLink together with AWS Direct Connect (option E) provides low-latency, private network connectivity from on premises to the Multi-Region Access Point with minimal operational overhead.

Option B, S3 Cross-Region Replication alone, would replicate data across Regions but would not automatically route customers to the closest Region; application changes and manual routing would be required, increasing operational overhead.

Option C would require building and maintaining a custom Lambda-based routing system, which is unnecessary when S3 Multi-Region Access Points provide managed routing and failover.

Option D, AWS Site-to-Site VPN, encrypts traffic over the public internet; it does not satisfy the explicit requirement to avoid public internet exposure and typically has less predictable latency than Direct Connect.

D is correct because thehealth check grace perioddefines how long Auto Scaling waits after launching an instance before checking its health status. With the larger content added to the S3 bucket, the instance initialization (via user data) is taking longer. Increasing the grace period allows the instance time to complete startup tasks before it’s marked as unhealthy.

Option A would not necessarily reduce startup time — the issue is likely network latency or the size of the content.

Option B only affects the timeout for health check responses, not the delay before they begin.

Option C is not applicable unless the health check path itself is invalid (which isn ' t mentioned).

https://docs.aws.amazon.com/AmazonS3/latest/userguide/ServerSideEncryptionCustomerKeys.html Clearly says we need following header for SSE-C x-amz-server-side​-encryption​-customer-algorithm Use this header to specify the encryption algorithm. The header value must be AES256.

Option A is correct because placing the EC2 instances behind an Application Load Balancer (ALB) and associating an SSL certificate from AWS Certificate Manager (ACM) with the ALB enables encryption in transit between the client and the ALB. Exporting the SSL certificate and installing it on each EC2 instance enables encryption in transit between the ALB and the web server. Configuring the ALB to listen on port 443 and to forward traffic to port 443 on the instances ensures that HTTPS is used for both connections. This solution achieves end-to-end encryption in transit for the web application12

Amazon SageMaker is a fully managed machine learning service that allows users to build, train, and deploy machine learning models quickly and easily1. Users can export their existing TensorFlow models and store the model artifacts in Amazon S3, a highly scalable and durable object storage service2. Users can then deploy the model to Amazon SageMaker and create an endpoint that can be invoked by the web application to provide recommendations3. This way, the solution can leverage the AI/ML capabilities of Amazon SageMaker without having to rewrite the personalization model.

AWS Elastic Beanstalk is a service that allows users to deploy and manage web applications without worrying about the infrastructure that runs those applications. Users can host their Java application in AWS Elastic Beanstalk and configure it to communicate with the Amazon SageMaker endpoint. This way, the solution can reduce the operational overhead of managing servers, load balancers, scaling, and application health monitoring.

AWS Database Migration Service (AWS DMS) is a service that helps users migrate databases to AWS quickly and securely. Users can use AWS DMS to migrate their SQL Server database to Amazon RDS for SQL Server, a fully managed relational database service that offers high availability, scalability, security, and compatibility. This way, the solution canreduce the operational overhead of managing database servers, backups, patches, and upgrades.

Option A is incorrect because using AWS Server Migration Service (AWS SMS) to migrate the on-premises physical application server and the web application VMs to AWS is not cost-effective or scalable. AWS SMS is a service that helps users migrate on-premises workloads to AWS. However, for this use case, migrating the physical application server and the web application VMs to AWS will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk and Amazon RDS.

Option C is incorrect because using AWS Application Migration Service to migrate the on-premises personalization model and VMs to Amazon EC2 instances in Auto Scaling groups is not cost-effective or scalable. AWS Application Migration Service is a service that helps users migrate applications from on-premises or other clouds to AWS without making any changes to their applications. However, for this use case, migrating the personalization model and VMs to EC2 instances will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk and Amazon RDS.

Option D is incorrect because containerizing the personalization model and the Java application and using Amazon Elastic Kubernetes Service (Amazon EKS) managed node groups to deploy them to Amazon EKS is not necessary or cost-effective. Amazon EKS is a service that allows users to run Kubernetes on AWS without needing to install, operate, and maintain their own Kubernetes control plane or nodes. However, for this use case, containerizing and deploying the personalization model and the Java application will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk. Moreover, using S3 Glacier Deep Archive as a storage class for images will incur a high retrieval fee and latency for accessing them.

Option C is correct because the business process is a multi-step workflow that needs state tracking: order received, package label printed, package scanned, and order marked as shipped. AWS Step Functions is the correct serverless orchestration service for coordinating these steps, while AWS Lambda performs event-driven work and DynamoDB stores order state. This removes the email-based manual workflow and avoids continuously running EC2 workers. AWS documentation describes Step Functions as a workflow service that can include human-in-the-loop and callback-style patterns, making it suitable for business processes that wait for external completion signals. AWS Batch is for batch compute jobs, not warehouse process orchestration. SQS with polling every 5 minutes is less direct and still introduces EC2 in option B. EFS plus instances is not a serverless modernization pattern.

https://docs.aws.amazon.com/drs/latest/userguide/what-is-drs.htmlhttps://docs.aws.amazon.com/drs/latest/userguide/recovery-workflow-gs.html

The requirement is not just foundation-model evaluation. It is full agent release validation: task completion, correct tool selection, final response quality, golden traces, continuous monitoring, and human audit. Amazon Bedrock AgentCore Evaluations is designed for automated assessment of agent and tool performance before and after deployment. Built-in evaluators can be used for common evaluation scenarios, custom evaluators can test organization-specific criteria, and online evaluations continuously monitor live traffic. That directly supports CI/CD quality gates and post-deployment degradation detection with less custom engineering. Option A creates a custom evaluation pipeline, increasing operational overhead. Option B evaluates only the underlying model, not the agent’s tool behavior. Option D covers online monitoring but misses pre-deployment golden-trace validation and custom evaluator use in the pipeline.

https://docs.aws.amazon.com/AmazonS3/latest/userguide/privatelink-interface-endpoints.html#types-of-vpc-endpoints-for-s3

https://aws.amazon.com/premiumsupport/knowledge-center/s3-bucket-access-direct-connect/

Use a private IP address over Direct Connect (with an interface VPC endpoint)

To access Amazon S3 using a private IP address over Direct Connect, perform the following steps:

3. Create a private virtual interface for your connection.

5. Create an interface VPC endpoint for Amazon S3 in a VPC that is associated with the virtual private gateway. The VGW must connect to a Direct Connect private virtualinterface. This interface VPC endpoint resolves to a private IP address even if you enable a VPC endpoint for S3.

https://aws.amazon.com/solutions/implementations/git-to-s3-using-webhooks/

https://medium.com/mindorks/building-webhook-is-easy-using-aws-lambda-and-api-gateway-56f5e5c3a596

Provision an Aurora Replica in a different Region will meet the requirement of the application being able to recover to a separate AWS Region in the event of an application failure, and no data can be lost, with the least amount of operational overhead.

The company should create an Amazon CloudFront distribution with the ALB as the origin. The company should add a custom header and random value on the CloudFront domain. The company should configure the ALB to conditionally forward traffic if the header and value match. The company should also deploy an AWS WAF web ACL that includes an appropriate rule group. The company should associate the web ACL with the Amazon CloudFront distribution. This solution will meet the requirements most cost-effectively because Amazon CloudFront is a fast content delivery network (CDN) service that securely deliversdata, videos, applications, and APIs to customers globally with lowlatency, hightransfer speeds, all within a developer-friendly environment1. By creating an Amazon CloudFront distribution with the ALB as the origin, the company can improve the performance andavailability of its application by caching static content at edge locations closer to end users. By adding a custom header and random value on the CloudFront domain, the company can prevent direct access to the ALB and ensure that only requests from CloudFront are forwarded to the ECS tasks. By configuring the ALB to conditionally forward traffic if the header and value match, the company can implement origin access identity (OAI) for its ALB origin. OAI is a feature that enables you to restrict access to your content by requiring users to access your content through CloudFront URLs2. By deploying an AWS WAF web ACL that includes an appropriate rule group, the company can prevent attacks and ensure business continuity with minimal service interruptions during an ongoing attack. AWS WAF is a web application firewall that lets you monitor and control web requests that are forwarded to your web applications. You can use AWS WAF to define customizable web security rules that control which traffic can access your web applications and which traffic should be blocked3. By associating the web ACL with the Amazon CloudFront distribution, the company can apply the web security rules to all requests that are forwarded by CloudFront.

The other options are not correct because:

Deploying the application in two AWS Regions and configuring Amazon Route 53 to route to both Regions with equal weight would not prevent attacks or ensure business continuity. Amazon Route 53 is a highly available and scalable cloud Domain Name System (DNS) web service that routes end users to Internet applications by translating names like www.example.com into numeric IP addresses4. However, routing traffic to multiple Regions would not protect against attacks or provide failover in case of an outage. It would also increase operational complexity and costs compared to using CloudFront and AWS WAF.

Configuring auto scaling for Amazon ECS tasks and creating a DynamoDB Accelerator (DAX) cluster would not prevent attacks or ensure business continuity. Auto scaling is a feature that enables you to automatically adjust your ECS tasks based on demand or a schedule. DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10x performance improvement. However, these features would not protect against attacks or provide failover in case of an outage. They would also increase operational complexity and costs compared to using CloudFront and AWS WAF.

Configuring Amazon ElastiCache to reduce overhead on DynamoDB would not prevent attacks or ensure business continuity. Amazon ElastiCache is a fully managed in-memory data store service that makes it easy to deploy, operate, and scale popular open-source compatible in-memory data stores. However, this service would not protect against attacks or provide failover in case of an outage. It would also increase operational complexity and costs compared to using CloudFront and AWS WAF.

The cleanest design is an AWS Config organization conformance pack deployed centrally. Conformance packs are YAML templates that can include AWS Config managed rules, custom rules, and remediation actions. AWS Config also supports managing conformance packs across all member accounts in an AWS Organizations organization, allowing centralized deployment, update, and deletion. The PutOrganizationConformancePack API deploys a conformance pack across member accounts and can create AWS Config rules and remediation actions without requiring member-account IAM permissions for those individual rule operations. Because the requirement includes four custom rules and five managed rules, the conformance pack must include all nine rules. Deploying only custom rules leaves the managed rules outside the update model. CloudFormation StackSets add unnecessary operational overhead and do not provide the same native organization conformance-pack governance.