Amazon Web Services SAP-C02 Dumps Questions Answers

The key requirements are: OpenSearch Service must be deployed within a VPC (VPC-only access), and developers must access OpenSearch from their local machines across multiple locations, including home networks. The most suitable low-overhead approach is to provide remote users with secure client-based connectivity into the VPC so they can reach private endpoints.

AWS Client VPN is a managed client-based VPN service that allows individual users to establish secure TLS VPN connections from their devices into a VPC. By associating a Client VPN endpoint with a subnet in the VPC and configuring authorization rules and routes, developers can access private resources (including VPC-only Amazon OpenSearch Service endpoints) as if they were on the corporate network. Client VPN is designed for distributed workforces and supports users connecting from anywhere without requiring each remote location to have dedicated network appliances.

Option A matches the need for remote developer access from home and multiple offices with the least operational overhead because it is a managed service for user-based VPN access and does not require running and maintaining bastion fleets or building site-to-site networks for each location.

Option B is not correct because AWS Site-to-Site VPN is designed to connect networks (for example, an office network or data center) to AWS, not to provide individual developers remote access from arbitrary home networks. Also, instructing developers to use an OpenVPN client does not align with how Site-to-Site VPN is typically used; Site-to-Site VPN terminates on a customer gateway device, not on individual laptops.

Option C is not correct because Direct Connect is designed for dedicated private connectivity between on-premises networks and AWS. It is not a solution for individual developers connecting from home. Additionally, using a public VIF is for reaching public AWS endpoints, whereas the requirement is to keep access within a VPC. A public VIF does not provide private VPC access to VPC-only service endpoints.

Option D is not the best choice because a bastion host provides SSH access to instances, not direct, secure network-level access to VPC-only managed service endpoints from developer tools. It also increases operational overhead (patching, hardening, monitoring, scaling) and introduces additional security considerations. Developers also typically need browser-based or tool-based access to OpenSearch Dashboards, which is better served by VPN access into the VPC than SSH tunneling through a bastion host as a primary access mechanism.

Therefore, configuring AWS Client VPN to provide developers with secure connectivity into the VPC is the correct solution.

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.

The current architecture uses a fleet of small EC2 instances that poll SQS for URLs and write results to EFS. Because URLs arrive only occasionally and each crawl takes 10 seconds or less, EC2 instances are often idle waiting for messages. This leads to unnecessary compute and storage costs.

This workload is naturally event-driven: each URL in the SQS queue triggers a short-lived crawl operation. AWS Lambda is well suited for such event-driven, short-duration tasks. Lambda integrates natively with Amazon SQS as an event source, which allows Lambda to poll the SQS queue automatically and invoke functions when messages arrive, scaling the concurrency up and down as needed. When there are no messages, there are no Lambda invocations and no compute charges, eliminating the cost of idle EC2 instances.

For storage of crawl results, Amazon S3 is a highly durable, cost-effective object store. The current use of an EFS file system mounted on all instances is no longer necessary if each Lambda invocation can write the output directly to S3 as objects (for example, CSV files). S3 charges only for the storage used and requests, and does not require continuously running file system infrastructure.

Option B replaces the idle fleet of EC2 instances with a Lambda function that reads from SQS. This aligns compute usage with actual work and takes advantage of serverless scaling and pricing.

Option E modifies the web-crawling process to store results in Amazon S3 instead of EFS, removing the need to maintain an EFS file system and its associated costs.

Option A increases instance size to m5.8xlarge, which greatly increases capacity and cost. Reducing the number of instances by 50% does not offset the large increase in instance size; it does not address idle capacity and likely increases overall cost.

Option C uses Amazon Neptune, which is a managed graph database service. It is not needed for storing simple CSV output; it is more complex and more expensive than S3 for this use case.

Option D uses Aurora Serverless MySQL. While Aurora Serverless can automatically scale, using a relational database to store simple crawl outputs adds unnecessary cost and operational complexity compared to S3 objects.

Therefore, moving the crawling logic to Lambda triggered by SQS (option B) and writing results directly to Amazon S3 (option E) meets the requirements in the most cost-effective way.