Professional-Cloud-DevOps-Engineer Google Cloud Certified - Professional Cloud DevOps Engineer Exam Questions and Answers

The correct answer is B. Store the password in Secret Manager and mount the secret as a volume within the application.

Secret Manager is a service that allows you to securely store and manage sensitive data such as passwords, API keys, certificates, and tokens.You can use Secret Manager to rotate your secrets automatically or manually, and access them from your Cloud Run applications1.

There are two ways to use secrets from Secret Manager in Cloud Run:

As environment variables: You can set environment variables that point to secrets in Secret Manager. Cloud Run will resolve the secrets at runtime and inject them into the environment of your application. However, this method has some limitations, such as:

The environment variables are cached for up to 10 minutes, so you may not get the latest version of the secret immediately.

The environment variables are visible in plain text in the Cloud Console and the Cloud SDK, which may expose sensitive information.

The environment variables are limited to 4 KB of data, which may not be enough for some secrets.2

As file system volumes: You can mount secrets from Secret Manager as files in a volume within your application. Cloud Run will create a tmpfs volume and write the secrets as files in it. This method has some advantages, such as:

The files are updated every 30 seconds, so you can get the latest version of the secret faster.

The files are not visible in the Cloud Console or the Cloud SDK, which provides better security.

The files can store up to 64 KB of data, which allows for larger secrets.3

Therefore, for your use case, it is better to use the second method and mount the secret as a file system volume within your application. This way, you can ensure that your application has the latest password, and you can deploy it with no downtime.

To mount a secret as a file system volume in Cloud Run, you can use the following command:

gcloud beta run deploy SERVICE --image IMAGE_URL --update-secrets=/path/to/file=secretName:version

where:

SERVICE is the name of your Cloud Run service.

IMAGE_URL is the URL of your container image.

/path/to/file is the path where you want to mount the secret file in your application.

secretName is the name of your secret in Secret Manager.

version is the version of your secret.You can uselatestto get the most recent version.3

You can also use the Cloud Console to mount secrets as file system volumes. For more details, seeMounting secrets from Secret Manager.

Cloud Build Private Pools allow your builds to run in a secure, isolated environment with direct access to resources inside your private VPC network, without exposing them to the public internet. This is the Google-recommended approach for minimizing management overhead while maintaining strong security boundaries.

From the official documentation:

"Private pools run your builds in a dedicated and secure environment that can connect to private network resources."

— Cloud Build Private Pools Overview

"You can configure private pools to access resources in a VPC network, which enables secure access to services without using public IPs."

— Accessing Private Resources

This method eliminates the need to create and manage additional compute instances or complex load balancing setups, providing seamless integration with your private services during CI pipeline execution.

The best options for furthering your application’s reliability goal while balancing velocity, reliability, and business needs are to have more frequent or potentially risky application releases and to tighten the SLO to match the application’s observed reliability. Having more frequent or potentially risky application releases can help you increase the change velocity and deliver new features faster. However, this also increases the likelihood of consuming more error budget and reducing the reliability of your service. Therefore, you should monitor your error budget consumption and adjust your release policies accordingly. For example, you can freeze or slow down releases when the error budget is low, or accelerate releases when the error budget is high. Tightening the SLO to match the application’s observed reliability can help you align your service quality with your users’ expectations and business needs. However, this also means that you have less room for error and need to maintain a higher level of reliability. Therefore, you should ensure that your SLO is realistic and achievable, and that you have sufficient engineering resources and processes to meet it.

Google Workload Identity is the recommended method to allow GKE workloads to securely access Google Cloud APIs using short-lived credentials without managing keys.

"Workload Identity is the recommended way to access Google Cloud services from GKE. It replaces the older method of using service account keys."

— Workload Identity Overview

"You can configure a Kubernetes service account to impersonate a Google Cloud service account by granting the roles/iam.workloadIdentityUser role and using the iam.gke.io/gcp-service-account annotation."

— Configure Workload Identity

This satisfies the customer’s requirement for PoLP and avoids long-lived keys.

The correct answer is B. Increase the memory limit in the application deployment.

The application is experiencing a memory leak, which means that it is allocating memory that is not freed or reused. This causes the heap usage to grow constantly until it reaches the memory limit of the pod, which triggers a restart by Kubernetes. Increasing the memory limit in the application deployment can help mitigate the problem by allowing the application to run longer before reaching the limit. However, this is not a permanent solution, as the memory leak will still occur and eventually exhaust the available memory.The best solution is to identify and fix the source of the memory leak in the application code, using tools like Cloud Profiler and pprof12.

https://sre.google/sre-book/managing-incidents/

Comprehensive and Detailed Explanation From General Google Cloud Knowledge:

App Hub allows you to organize and discover services and applications within your Google Cloud environment. For App Hub to recognize and display resources as components of an "application," these resources often need to be explicitly registered or discovered as "services" within that application's configuration. While App Hub can automatically discover some resources (like GKE workloads, Cloud Run services), for other resources, or to establish specific relationships, manual registration or more detailed configuration is sometimes required.

Option A (Attach the project containing the forwarding rule as an App Hub service project): While App Hub works across projects (host project for the application, service projects for services and workloads), simply attaching the project might not be sufficient for App Hub to automatically pick up and categorize every resource like a forwarding rule specifically for a defined application without further context. The forwarding rule needs to be associated with a service within the App Hub application.

Option B (Enable the App Hub API in the project containing the forwarding rule): The App Hub API needs to be enabled in projects where you want to manage App Hub resources (applications, services, workloads). If it wasn't enabled, you likely wouldn't be able to see any resources from that project. Since other resources are visible, this is less likely the root cause for a single missing resource, though it's a prerequisite for App Hub to function at all with that project.

Option C (Configure the forwarding rule to forward to the correct target proxy): While correct configuration of the forwarding rule is essential for its operational functionality, App Hub's ability to list the forwarding rule is more about its discovery and registration within App Hub's model rather than its traffic-directing correctness. An incorrectly configured forwarding rule that is properly registered might still appear in App Hub, perhaps with an error status.

Option D (Register the forwarding rule as a service in the application configuration): App Hub applications are composed of "services," and these services are in turn composed of "workloads" or other discovered/registered resources. A forwarding rule is typically an entry point or part of the infrastructure for a service. Explicitly registering it or the resource it points to (which then allows App Hub to trace back to the forwarding rule) as a service or part of a service within the application configuration would make it visible and properly cataloged by App Hub. App Hub discovers resources by looking for specific labels or by manual registration. If it's not automatically discovered as part of a recognized workload (like a GCE instance group service exposed via a load balancer), explicit registration is often the way to make it appear.

Reference (Based on general App Hub functionality):

App Hub discovers resources that are part of registered applications and their services. Services in App Hub can be based on various Google Cloud resources. If a resource like a forwarding rule isn't automatically linked to a displayed workload, it might need to be explicitly defined as a service or part of a service.

From the Google Cloud documentation on App Hub concepts:

"Applications are the core organizational unit in App Hub. An application represents a logical system that delivers business value... Services represent the logical components of an application... Workloads are instances of your services running on Google Cloud infrastructure. App Hub automatically discovers workloads for supported resource types or you can manually register them."

Forwarding rules are associated with load balancing, which exposes services. If the service that the forwarding rule points to is correctly registered and identified by App Hub, associated infrastructure like the forwarding rule should typically be discoverable. If it's not, ensuring the service it fronts is correctly registered and that App Hub understands this link is key. Option D aligns with this concept of ensuring the relevant component (which the forwarding rule is part of) is registered within the application structure.

You can find more information in the official Google Cloud documentation regarding App Hub:

App Hub overview: https://cloud.google.com/app-hub/docs/overview

Registering services and workloads: Documentation would detail how different resources are discovered or need to be registered.

The first thing you should do to prepare your ecommerce application for the upcoming peak traffic season is to load test the application to profile its performance for scaling. Load testing is a process of simulating high traffic or user demand on your application and measuring how it responds.Load testing can help you identify any bottlenecks, errors, or performance issues that might affect your application during the busy season1.Load testing can also help you determine the optimal scaling strategy for your application, such as horizontal scaling (adding more instances) or vertical scaling (adding more resources to each instance)2.

There are different tools and methods for load testing your ecommerce application on Google Cloud, depending on the type and complexity of your application.For example, you can use Cloud Load Balancing to distribute traffic across multiple instances of your application, and use Cloud Monitoring to measure the latency, throughput, and error rate of your application3.You can also use Cloud Functions or Cloud Run to create serverless load generators that can simulate user requests and send them to your application4. Alternatively, you can use third-party tools such as Apache JMeter or Locust to create and run load tests on your application.

By load testing your ecommerce application before the peak traffic season, you can ensure that your application is ready to handle the expected load and provide a good user experience. You can also use the results of your load tests to plan and implement other steps to prepare your application for the busy season, such as migrating to a more scalable platform, creating a Terraform configuration for deploying to additional regions, or pre-provisioning additional compute power.

Comprehensive and Detailed Explanation From General Cloud Monitoring Knowledge:

The key requirement is to distinguish between failures in your own service and those caused by an underlying Google Cloud service.

A. Enable Personalized Service Health annotations on the dashboard: Google Cloud Personalized Service Health provides information about incidents affecting Google Cloud services that may impact your projects. When enabled and integrated with Monitoring, it can display these events as annotations on your dashboards, overlaying them on your service's metrics charts. This allows you to correlate dips in your service's performance with known Google Cloud service issues, directly addressing the need to distinguish failure origins.

B. Create an alerting policy for the system error metrics: Alerting policies are for notifications when metrics cross thresholds. While useful for detecting issues in your own service, they don't inherently distinguish the cause between your service and a Google Cloud dependency without further context, which option A provides.

C. Create a log-based metric to track cloud service errors, and display the metric on the dashboard: You could try to create log-based metrics from logs that might indicate a cloud service error (e.g., specific API error codes from Google Cloud services). However, this is indirect, might require complex parsing, and Personalized Service Health is a more direct and authoritative source for Google Cloud service disruptions.

D. Create a logs widget to display system errors from Cloud Logging on the dashboard: Similar to C, displaying raw system error logs can be helpful for troubleshooting your own service, but it doesn't provide a clear, curated view of whether a Google Cloud service itself is having an issue. It would require manual interpretation to link these logs to a potential Google Cloud outage.

Personalized Service Health is specifically designed to provide visibility into Google Cloud service incidents relevant to your resources. Integrating this with Monitoring dashboards is the most direct way to achieve the stated goal.

Reference (Based on Cloud Monitoring and Personalized Service Health features):

Personalized Service Health Overview: https://cloud.google.com/service-health/docs/overview

Integrating with Cloud Monitoring: Documentation often shows how to enable annotations for Personalized Service Health events on Monitoring charts. This allows a visual correlation between your service metrics and Google Cloud service health events."Personalized Service Health integrates with Cloud Monitoring so you can see service health events alongside your metrics."

"You can enable annotations on your metric charts to display relevant Personalized Service Health events."

This feature directly helps differentiate between issues in your application versus issues in the underlying Google Cloud services.

The best option for providing your security team with the necessary permissions following the principle of least privilege and Google-recommended practices is to assign the roles/logging.privateLogViewer role to a group with all the security team members. The roles/logging.privateLogViewer role is a predefined role that grants read-only access to Data Access audit logs and other private logs in Cloud Logging. A group is a collection of users that can be assigned roles and permissions as a single unit. You can assign the roles/logging.privateLogViewer role to a group with all the security team members by using IAM policies. This way, you can provide your security team with the minimum level of access they need to view Data Access audit logs in the _Required bucket.

The best options for defining triggers that indicate whether an incident requires a postmortem based on Site Reliability Engineering (SRE) practices are an external stakeholder asks for a postmortem and data is lost due to an incident. An external stakeholder is someone who is affected by or has an interest in the service, such as a customer or a partner. If an external stakeholder asks for a postmortem, it means that they are concerned about the impact or root cause of the incident, and they expect an explanation and remediation from the service provider. Therefore, this should trigger a postmortem to address their concerns and improve their satisfaction. Data loss is a serious consequence of an incident that can affect the integrity and reliability of the service. If data is lost due to an incident, it means that there was afailure in the backup or recovery mechanisms, or that there was a corruption or deletion of data. Therefore, this should trigger a postmortem to investigate the cause and impact of the data loss, and to prevent it from happening again.

The correct answer is D. Grant the logging.logWriter and monitoring.metricWriter roles to the Compute Engine service accounts.

According to the Google Cloud documentation, the Compute Engine service account is a Google-managed service account that is automatically created when you enable the Compute Engine API1.This service account is used by default to run your Compute Engine instances and access other Google Cloud services on your behalf1.To ensure that monitoring metrics and logs for the instances are visible in Cloud Logging and Cloud Monitoring, you need to grant the following IAM roles to the Compute Engine service account23:

The logging.logWriter role allows the service account to write log entries to Cloud Logging4.

The monitoring.metricWriter role allows the service account to write custom metrics to Cloud Monitoring5.

These roles grant the minimum permissions that are needed for logging and monitoring, following the principle of least privilege. The other roles are either unnecessary or too broad for this purpose.For example, the logging.editor role grants permissions to create and update logs, log sinks, and log exclusions, which are not required for writing log entries6. The logging.admin role grants permissions to delete logs, log sinks, and log exclusions, which are not required for writing log entries and may pose a security risk if misused. The monitoring.editor role grants permissions to create and update alerting policies, uptime checks, notification channels, dashboards, and groups, which are not required for writing custom metrics.

The best option for centralizing the logs generated by Google Cloud services from all projects only inside your production folder is to create an aggregated log sink associated with the production folder that uses a Cloud Logging bucket as the destination. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. A Cloud Logging bucket is a storage location for logs that can be used as a destination for log sinks. By creating an aggregated log sink with a Cloud Logging bucket, you can collect and store all the logs from the production folder in one place and allow for alerting and near-real time analysis using Cloud Monitoring and Cloud Operations.

To securely connect Cloud Build to an AlloyDB cluster using a private IP address and adhere to Google-recommended security practices, you need to address two main aspects:

Network Connectivity:Ensuring Cloud Build can reach the private IP of the AlloyDB cluster.

Authentication/Credential Management:Securely authenticating Cloud Build to the AlloyDB cluster.

Let's break down why Option B is the most suitable:

Cloud Build Private Pool:AlloyDB is accessed via a private IP in your VPC. Cloud Build's default build environment runs on Google-managed infrastructure outside your VPC and cannot directly access private IP addresses. To enable this, you must use aCloud Build private pool. A private pool can be configured with VPC peering to your default VPC, allowing build steps running within that pool to access resources like your AlloyDB cluster via their private IPs. Option B correctly includes "execute the schema migration script in a private pool."  

Service Account with Permissions (IAM Database Authentication):AlloyDB supports IAM database authentication. This is a Google-recommended security practice because it allows you to manage database access using Google Cloud's Identity and Access Management (IAM) rather than relying on traditional database passwords.  

You would create a dedicated service account for Cloud Build (or use the private pool's service account).

This service account would be granted the necessary IAM roles to connect to the AlloyDB instance (e.g., roles/alloydb.client) and a database-level IAM role for login (e.g., roles/alloydb.user or roles/alloydb.admin depending on the permissions needed for schema migration).  

Cloud Build would then be configured to use this service account. The "permission to access the database" in Option B refers to these IAM permissions. This method avoids managing and distributing database passwords.

Analyzing the options:

A. Set up a Cloud Build private pool to access the database through a static external IP address...

While using a private pool is correct for network access, routing this through a staticexternalIP for a resource that has aprivateIP is generally not the first-choice secure pattern if direct private access is feasible. It adds complexity and a potential external exposure point, even if firewalled. The aim is to keep traffic within the private network as much as possible.

B. Create a service account that has permission to access the database. Configure Cloud Build to use this service account and execute the schema migration script in a private pool.

This option correctly combines the use of aprivate pool(for private IP network access) with aservice account having permissions(strongly implying IAM database authentication for AlloyDB, which is a best practice). This is a secure and robust approach.

C. Add the database username and encrypted password to the application configuration file...

Storing credentials, even if "encrypted" (the method and key management for encryption are unspecified and problematic), in application configuration files checked into source control or packaged with the application is a significant security risk and not a recommended practice.

D. Add the database username and password to Secret Manager. When running the schema migration script, retrieve the username and password from Secret Manager.

UsingSecret Managerto store database usernames and passwords is a Google-recommended practiceifyou are using password-based authentication. However, this optionalonedoes not solve the network connectivity issue for Cloud Build to reach the private IP of AlloyDB. You would still need a private pool. While D is good for secret management, B offers a more comprehensive solution that includes both the network aspect and implies a more modern authentication method (IAM database auth). If the question forced a choice between only doing secure credential storage (D) or doing IAM auth + private networking (B), B is more complete for the overall task.  

Conclusion:Option B is the most aligned with Google-recommended security practices as it addresses both the necessary private network connectivity via a Cloud Build private pool and promotes the use of IAM-based database authentication for AlloyDB, which is generally preferred over managing passwords.

References (General Concepts):

Cloud Build Private Pools for VPC Access:Google Cloud documentation for Cloud Build explicitly details using private pools to connect to resources in a VPC network.

See:https://www.google.com/search?q=https://cloud.google.com/build/docs/private-pools/accessing-private-resources-with-private-pools

AlloyDB IAM Database Authentication:Google Cloud documentation for AlloyDB highlights IAM database authentication as a secure method.

See:https://www.google.com/search?q=https://cloud.google.com/alloydb/docs/iam-authentication

Secret Manager:If password authentication were the only option, Secret Manager would be the recommended way to store those credentials.

See:https://cloud.google.com/secret-manager

Option B synergizes the benefits of private networking and modern IAM-based authentication for a comprehensive secure solution.

https://cloud.google.com/monitoring/support/notification-options#creating_channels

To configure SMS notifications, do the following:

In the SMS section, click Add new and follow the instructions. Click Save. When you set up your alerting policy, select the SMS notification type and choose a verified phone number from the list.

Cloud Workstations provides a secure way to preview web applications using built-in preview links, which expose internal ports only via authenticated and authorized access — no public IP exposure required.

“To preview your app, click the preview icon in the Code OSS panel, which securely tunnels the service to your browser.”

— Cloud Workstations - Previewing your app

This adheres to Google's security best practices by avoiding public IPs or manual tunneling while enabling developer efficiency.

For a postmortem to be truly blameless, it must focus on identifying the contributing causes of the incident without indicting any individual or team for bad or inappropriate behavior.

https://cloud.google.com/architecture/customizing-stackdriver-logs-fluentd

Besides the list of default logs that the Logging agent streams by default, you can customize the Logging agent to send additional logs to Logging or to adjust agent settings by adding input configurations. The configuration definitions in these sections apply to the fluent-plugin-google-cloud output plugin only and specify how logs are transformed and ingested into Cloud Logging.https://cloud.google.com/logging/docs/agent/logging/configuration#configure

The correct answer is D. Option D.

According to the DevOps best practices, a joint-ownership model for a service between the DevOps team and the Software Development team should follow these principles12:

The DevOps team and the Software Development team should share the responsibility and collaboration for managing the service infrastructure, performing code reviews, and adopting and sharing SLOs for the service.

The DevOps team and the Software Development team should have end-to-end ownership of the service, from design to development to deployment to operation to maintenance.

The DevOps team and the Software Development team should use common tools and processes to facilitate communication, coordination, and feedback.

The DevOps team and the Software Development team should align their goals and incentives with the business outcomes and customer satisfaction.

Option D is the only option that reflects these principles. Option D assigns both teams the responsibilities of managing the service infrastructure, performing code reviews, and adopting and sharing SLOs for the service. Option D also implies that both teams have end-to-end ownership of the service, as they are involved in every stage of the service lifecycle.Option D also encourages both teams to use common tools and processes, such as GitLab3, to collaborate and communicate effectively. Option D also aligns both teams with the business outcomes and customer satisfaction, as they use SLOs to measure and improve the service quality.

The other options are incorrect because they do not follow the DevOps best practices. Option A is incorrect because it assigns only the DevOps team the responsibility of managing the service infrastructure, which creates a silo between the two teams and reduces their collaboration. Option A also does not assign any responsibility for adopting and sharing SLOs for the service, which means that both teams lack a common metric for measuring and improving the service quality. Option B is incorrect because it assigns only the Software Development team the responsibility of performing code reviews, which creates a gap between the two teams and reduces their feedback. Option B also does not assign any responsibility for adopting and sharing SLOs for the service, which means that both teams lack a common metric for measuring and improving the service quality. Option C is incorrect because it assigns both teams the same responsibilities as option A and option B, which combines their drawbacks.

The correct answer is C. Configure Cloud Profiler, and initialize the cloud.google.com/go/profiler library in the application.

According to the Google Cloud documentation, Cloud Profiler is a statistical, low-overhead profiler that continuously gathers CPU usage and memory-allocation information from your production applications1. Cloud Profiler can help you identify slow paths in your code and optimize the performance of your applications. Cloud Profiler supports applications written in Go that run on App Engine standard environment2. To use Cloud Profiler, you need to configure it in your Google Cloud project and initialize the cloud.google.com/go/profiler library in your application code3. You can then use the Cloud Profiler interface to analyze the profiling data and visualize the results by using flame graphs4. Cloud Profiler has minimal performance impact and management overhead, as it only samples a small fraction of the application activity and does not require any additional infrastructure or agents.

The other options are incorrect because they do not meet the requirements of minimizing performance impact and management overhead. Option A is incorrect because it requires installing a continuous profiling tool into Compute Engine, which is an additional infrastructure that needs to be managed and maintained. Option B is incorrect because it requires periodically running the go tool pprof command against the application instance, which is a manual and disruptive process that can affect the application performance. Option D is incorrect because it only uses Cloud Monitoring to assess the App Engine CPU utilization metric, which is not enough to identify slow paths in the code or optimize the application performance.

The correct answer is A. Notify the team that their error budget is used up. Negotiate with the team for a launch freeze or tolerate a slightly worse user experience.

According to the Site Reliability Engineering (SRE) practices, an error budget is the amount of unreliability that a service can tolerate without harming user satisfaction1.An error budget is derived from the service-level objectives (SLOs), which are the measurable goals for the service quality2. When a service exceeds its error budget, it means that it has violated its SLOs and may have negatively impacted the users.In this case, the SRE team should notify the product team that their error budget is used up and negotiate with them for a launch freeze or a lower SLO3. A launch freeze means that no new features are deployed until the service reliability is restored. A lower SLO means that the product team accepts a slightly worse user experience in exchange for launching new features. Both options require a trade-off between reliability and innovation, and should be agreed upon by both teams.

The other options are incorrect because they do not follow the SRE practices.Option B is incorrect because it violates the principle of error budget autonomy, which means that each service should have its own error budget and SLOs, and should not borrow or reallocate them from other services4. Option C is incorrect because it does not address the root cause of the error budget overspend, and may create unrealistic expectations for the service reliability. Option D is incorrect because it does not prevent the possibility of introducing new errors or bugs with the feature launch, which may further degrade the service quality and user satisfaction.

The best option for securing the CI/CD deployment pipeline is to configure vulnerability analysis with Artifact Registry and Binary Authorization. Vulnerability analysis is a feature that allows you to scan container images for known vulnerabilities and security issues. You can use vulnerability analysis with Artifact Registry, which is a service that allows you to store and manage container images and other artifacts. By using vulnerability analysis with Artifact Registry, you can ensure that your container images are scanned for vulnerabilities before they are deployed. Binary Authorization is a feature that allows you to enforce signature-based validation when deploying container images. You can use Binary Authorization with Cloud Build, which is a service that allows you to build and deploy container images. By using Binary Authorization with Cloud Build, you can ensure that only authorized and verified container images are deployed to your environment.

https://cloud.google.com/architecture/prep-kubernetes-engine-for-prod#roles_and_groups

The best options for reducing toil in the pipeline and minimizing the amount of time it takes to complete an end-to-end deployment are to create a trigger to notify the required team to complete the next step when manual intervention is required and to automate promotion approvals from the development environment to the test environment. A trigger is a resource that initiates a deployment when an event occurs, such as a code change, a schedule, or a manual request. You can create a trigger to notify the required team to complete the next step when manual intervention is required by using Cloud Build or Cloud Functions. This way, you can reduce the waiting time and human errors in the pipeline. A promotion approval is a process that allows you to approve or reject a deployment from one environment to another, such as from development to test. You can automate promotion approvals from the development environment to the test environment by using Google Cloud Deploy or Cloud Build. This way, you can speed up the deployment process and avoid manual steps.

The correct answer is B. Apply the constraints/iam.disableServiceAccountKeyCreation constraint to the organization.

According to the Google Cloud documentation, the constraints/iam.disableServiceAccountKeyCreation constraint is an organization policy constraint that prevents the creation of user-managed service account keys1.User-managed service account keys are long-lived credentials that can be downloaded as JSON or P12 files and used to authenticate as a service account2.These keys pose severe security risks if they are leaked, stolen, or misused by unauthorized entities34.By applying this constraint to the organization, you can completely eliminate the risks associated with the use of JSON service account keys and enforce a more secure alternative for authentication, such as Workload Identity or short-lived access tokens12. This also minimizes operational overhead by avoiding the need to manage, rotate, or revoke user-managed service account keys.

The other options are incorrect because they do not completely eliminate the risks associated with the use of JSON service account keys. Option A is incorrect because it only restricts the IAM permissions to create, list, get, delete, or sign service account keys, but it does not prevent existing keys from being used or leaked. Option C is incorrect because it only disables the upload of user-managed service account keys, but it does not prevent the creation or download of such keys. Option D is incorrect because it only limits the IAM role that can create and manage service account keys, but it does not prevent the keys from being distributed or exposed to unauthorized entities.

https://cloud.google.com/error-reporting/docs/formatting-error-messages

https://cloud.google.com/error-reporting/docs/reference/libraries#client-libraries-install-python

no need to install error reporting library on App Engine Flex.

The correct answer is C. Change the machine type for your VMs to n2-standard-8.

According to the Google Cloud documentation, the network throughput performance of a Compute Engine VM depends on its machine type1. The n2-standard-2 machine type has a maximum egress bandwidth of 4 Gbps, which can be a bottleneck for serving large files. By changing the machine type to n2-standard-8, you can increase the maximum egress bandwidth to 16 Gbps, which can improve the network throughput performance and reduce the download time for users.You also need to enable per VM Tier_1 networking performance, which is a feature that allows VMs to achieve higher network performance than the default settings2.

The other options are incorrect because they do not improve the network throughput performance of your VMs.Option A is incorrect because Cloud NAT is a service that allows private IP addresses to access the internet, but it does not increase the network bandwidth or speed3.Option B is incorrect because adding additional network interfaces (NICs) or IP addresses per NIC does not increase ingress or egress bandwidth for a VM1.Option D is incorrect because deploying the Ops Agent can help you monitor and troubleshoot your VMs, but it does not affect the network throughput performance4.

Reason : Incident has not occurred yet, even when development team is already pushing new features multiple times a week. The option A says, to define an error budget "policy", not to define error budget(It is already present). Just simple means to bring in all stakeholders, and decide how to consume the error budget effectively that could bring balance between feature deployment and reliability.

The goals of this policy are to: -- Protect customers from repeated SLO misses -- Provide an incentive to balance reliability with other featureshttps://sre.google/workbook/error-budget-policy/

https://sre.google/sre-book/service-level-objectives/

The best option for preparing to handle the predicted growth is to verify the maximum node pool size, enable a Horizontal Pod Autoscaler, and then perform a load test to verify your expected resource needs. The maximum node pool size is a parameter that specifies the maximum number of nodes that can be added to a node pool by the cluster autoscaler. You should verify that the maximum node pool size is sufficient to accommodate your expected growth rate and avoid hitting any quota limits. The Horizontal Pod Autoscaler is a feature that automatically adjusts the number of Pods in a deployment or replica set based on observed CPU utilization or custom metrics. You should enable a Horizontal Pod Autoscaler for your application to ensure that it runs enough Pods to handle the load. A load test is a test that simulates high user traffic and measures the performance and reliability of your application. You should perform a load test to verify your expected resource needs and identify any bottlenecks or issues.

The best option for minimizing the number of log sinks created and ensuring that the log sinks apply to future projects is to create an aggregated log sink in the Dev and Prod folders. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. By creating an aggregated log sink in each folder, you can export log entries for development projects to dev_dataset and log entries for production projects to prod_dataset. You can also use filters to specify which logs you want to export. Additionally, by creating an aggregated log sink at the folder level, you can ensure that the log sink applies to future projects that are created under that folder.

The best option for selecting a deployment and testing strategy that meets your requirements is to use blue/green deployment and canary testing. A blue/green deployment is a deployment strategy that involves creating two identical environments, one running the current version of the application (blue) and one running the new version of the application (green). The traffic is switched from blue to green after testing the new version, and if any issues are discovered, the traffic can be switched back to blue instantly. This way, you can reduce the complexity of release deployments and minimize the duration of deployment rollbacks. A canary testing is a testing strategy that involves releasing a new version of an application to a subset of users or servers and monitoring its performance and reliability. This way, you can test real production traffic with a gradual increase in the number of affected users.

The best option for configuring Cloud Logging to log each IP address that accesses the API by using the fewest number of steps is to enable logging on the firewall rule. A firewall rule is a rule that controls the traffic to and from your Compute Engine instances. You can enable logging on a firewall rule to capture information about the traffic that matches the rule, such as source and destination IP addresses, protocols, ports, and actions. You can use Cloud Logging to view and export the firewall logs to other destinations, such as BigQuery, for further analysis.

The most effective ways to measure application reliability from a user perspective without making any engineering changes are to create new synthetic clients to simulate a user journey using the application, and to use current and historic Request Logs to trace customer interaction with the application. These methods can help you monitor the availability, latency, and errors of your application from an end-user perspective .

The most cost-effective network tier and load balancing configuration for your frontend tier is to use Premium Tier with a regional load balancer. Premium Tier is a network tier that provides high-performance and low-latency network connectivity across Google’s global network. A regional load balancer is a load balancer that distributes traffic within a single region. Since your application is deployed entirely within the europe-west2 region and only serves users based in the United Kingdom, you can use Premium Tier with a regional load balancer to optimize the network performance and cost.

The best option for implementing the rollback actions is to update the Kubernetes Service to point to the previous Kubernetes Deployment. A Kubernetes Service is a resource that defines how to access a set of Pods. A Kubernetes Deployment is a resource that manages the creation and update of Pods. By using the blue/green deployment methodology, you can create two Deployments, one for the current version (blue) and one for the new version (green), and use a Service to switch traffic between them. If you need to rollback, you can update the Service to point to the previous Deployment (blue) and stop sending traffic to the new Deployment (green).

https://cloud.google.com/logging/docs/audit#access-control

SLIs are quantitative measures of service behavior. For reliability, success rate (successful vs. total requests) is the most fundamental metric.

“The most common SLIs are request success rate, availability, and latency.”

— Google SRE Workbook: Implementing SLIs

“Availability SLIs often look like ‘the fraction of requests that succeed.’”

— Google SRE Book

Option C directly maps to this definition and is the most accurate and measurable indicator of overall reliability.

Scaling App Engine scales the number of instances automatically in response to processing volume. This scaling factors in the automatic_scaling settings that are provided on a per-version basis in the configuration file. A service with basic scaling is configured by setting the maximum number of instances in the max_instances parameter of the basic_scaling setting. The number of live instances scales with the processing volume. You configure the number of instances of each version in that service's configuration file. The number of instances usually corresponds to the size of a dataset being held in memory or the desired throughput for offline work. You can adjust the number of instances of a manually-scaled version very quickly, without stopping instances that are currently running, using the Modules API set_num_instances function.https://cloud.google.com/appengine/docs/standard/python/how-instances-are-managed

https://cloud.google.com/appengine/docs/standard/python/config/appref

max_idle_instances Optional. The maximum number of idle instances that App Engine should maintain for this version. Specify a value from 1 to 1000. If not specified, the default value is automatic, which means App Engine will manage the number of idle instances. Keep the following in mind: A high maximum reduces the number of idle instances more gradually when load levels return to normal after a spike. This helps your application maintain steady performance through fluctuations in request load, but also raises the number of idle instances (and consequent running costs) during such periods of heavy load.

The best option for ensuring that the three environments are consistent and following Google-recommended practices is to use Cloud Build to render and deploy the network policies and the DaemonSet, and set up Config Sync to sync the configurations for the three environments. Cloud Build is a service that executes your builds on Google Cloud infrastructure. You can use Cloud Build to render and deploy your network policies and DaemonSet as code using tools like Kustomize, Helm, or kpt. Config Sync is a feature that enables you to manage the configurations of your GKE clusters from a single source of truth, such as a Git repository. You can use Config Sync to sync the configurations for your development, staging, and production environments and ensure that they are consistent.

The best option for giving developers the ability to test the latest revisions of the service before the service is exposed to customers is to run the gcloud run deploy booking-engine --no-traffic --tag devcommand and use the https://dev----booking-engine-abcdef.a.run.app URL for testing. The gcloud run deploy command is a command that deploys a new revision of your service or updates an existing service. By using the --no-traffic flag, you can prevent any traffic from being sent to the new revision. By using the --tag flag, you can assign a tag to the new revision, such as dev. This way, you can create a new revision of your service without affecting your customers. You can also use the tag-based URL (e.g., https://dev----booking-engine-abcdef.a.run.app) to access and test the new revision.

Create log views for each project team, and only show each project team their application logs. Grant the operations team access to the _AllLogs View in the central logging project1.

This approach aligns with the Google Cloud’s recommended methodologies for Professional Cloud DevOps Engineers1. Log views allow you to create and manage access control at a finer granularity for your logs. By creating a separate log view for each project team, you can ensure that they only have access to their respective logs. The operations team, on the other hand, can be granted access to the _AllLogs view in the central logging project, allowing them to view all logs as required.

This solution not only meets the security team’s requirements but also minimizes costs as it leverages built-in features of Google Cloud’s logging and does not require exporting logs to another service like BigQuery (as suggested in option A), which could incur additional costs1.

 The best option for optimizing your cloud spend by ensuring that only a single instance of your infrastructure stack exists at a time is to confirm that the pipeline is storing and retrieving the terraform.tfstate file from Cloud Storage with the Terraform gcs backend. The terraform.tfstate file is a file that Terraform uses to store the current state of your infrastructure. The Terraform gcs backend is a backend type that allows you to store the terraform.tfstate file in a Cloud Storage bucket. By using the Terraform gcs backend, you can ensure that your pipeline has access to the latest state of your infrastructure and avoid creating multiple copies of the entire infrastructure stack.