PCDOE practice questions 191–200

1. Question 191 · 210 in bank

   Your team deploys applications to three Google Kubernetes Engine (GKE) environments: development, staging, and production. You use GitHub repositories as your source of truth. You need to ensure that the three environments are consistent. You want to follow Google-recommended practices to enforce and install network policies and a logging DaemonSet on all the GKE clusters in those environments. What should you do?

   • AUse Google Cloud Deploy to deploy the network policies and the DaemonSet. Use Cloud Monitoring to trigger an alert if the network policies and DaemonSet drift from your source in the repository.
   • BUse Google Cloud Deploy to deploy the DaemonSet and use Policy Controller to configure the network policies. Use Cloud Monitoring to detect drifts from the source in the repository and Cloud Functions to correct the drifts.
   • CUse Cloud Build to render and deploy the network policies and the DaemonSet. Set up Config Sync to sync the configurations for the three environments.
   • DUse Cloud Build to render and deploy the network policies and the DaemonSet. Set up a Policy Controller to enforce the configurations for the three environments.

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   Answer: D

   Explanation

   Option C is not as effective as Option D because it does not enforce the network policies and DaemonSet configurations. This means that unauthorized changes could still be made to the configurations. Config Sync is a tool that can be used to synchronize Kubernetes configurations across multiple clusters. However, it does not prevent unauthorized changes from being made to the configurations. Policy Controller is a tool that can be used to enforce Kubernetes configurations. It does this by monitoring the Kubernetes API for changes to the configurations and automatically reverting unauthorized changes. Therefore, Option D is a more secure and reliable option for ensuring that the network policies and DaemonSet are enforced and installed consistently across the three environments.

   Source: Adapted from community discussion — ManishKS

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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2. Question 192 · 210 in bank

   You are using Terraform to manage infrastructure as code within a CI/CD pipeline. You notice that multiple copies of the entire infrastructure stack exist in your Google Cloud project, and a new copy is created each time a change to the existing infrastructure is made. You need to optimize your cloud spend by ensuring that only a single instance of your infrastructure stack exists at a time. You want to follow Google-recommended practices. What should you do?

   • ACreate a new pipeline to delete old infrastructure stacks when they are no longer needed.
   • BConfirm that the pipeline is storing and retrieving the terraform.tfstate file from Cloud Storage with the Terraform gcs backend.
   • CVerify that the pipeline is storing and retrieving the terraform.tfstate file from a source control.
   • DUpdate the pipeline to remove any existing infrastructure before you apply the latest configuration.

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   Answer: B

   References

   • Google Cloud documentation

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3. Question 193 · 210 in bank

   You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis. Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects. Log entries for development projects must be exported to dev_dataset, and log entries for production projects must be exported to prod_dataset. You need to minimize the number of log sinks created, and you want to ensure that the log sinks apply to future projects. What should you do?

   • ACreate a single aggregated log sink at the organization level.
   • BCreate a log sink in each project.
   • CCreate two aggregated log sinks at the organization level, and filter by project ID.
   • DCreate an aggregated log sink in the Dev and Prod folders.

   Show answer

   Answer: D

   Explanation

   To minimize the number of log sinks and ensure that the log sink configurations are applied to future projects, the optimal choice is Option D. By creating an aggregated log sink at the folder level for both the Dev and Prod folders, you can enforce consistent export configurations for all existing and forthcoming projects within each folder. This approach facilitates centralized management, eliminating the need for creating individual sinks for each project. It reduces complexity and ensures that any new projects added to the Dev or Prod folders will automatically adopt the log sink configuration, streamlining the process and fostering uniformity throughout the organization.

   Source: Adapted from community discussion — xhilmi

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4. Question 194 · 210 in bank

   Your company runs services by using multiple globally distributed Google Kubernetes Engine (GKE) clusters. Your operations team has set up workload monitoring that uses Prometheus-based tooling for metrics, alerts, and generating dashboards. This setup does not provide a method to view metrics globally across all clusters. You need to implement a scalable solution to support global Prometheus querying and minimize management overhead. What should you do?

   • AConfigure Prometheus cross-service federation for centralized data access.
   • BConfigure workload metrics within Cloud Operations for GKE.
   • CConfigure Prometheus hierarchical federation for centralized data access.
   • DConfigure Google Cloud Managed Service for Prometheus.

   Show answer

   Answer: D

   Explanation

   Google Cloud Managed Service for Prometheus is Google Cloud's fully managed, multi-cloud, cross-project solution for Prometheus metrics. It lets you globally monitor and alert on your workloads, using Prometheus, without having to manually manage and operate Prometheus at scale. Managed Service for Prometheus collects metrics from Prometheus exporters and lets you query the data globally using PromQL, meaning that you can keep using any existing Grafana dashboards, PromQL-based alerts, and workflows. It is hybrid- and multi-cloud compatible, can monitor both Kubernetes and VM workloads, retains data for 24 months, and maintains portability by staying compatible with upstream Prometheus. Reference: https://cloud.google.com/stackdriver/docs/managed-prometheus

   Source: Adapted from community discussion — PrayasMohanty

   References

   • Google Cloud documentation

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5. Question 195 · 210 in bank

   You need to build a CI/CD pipeline for a containerized application in Google Cloud. Your development team uses a central Git repository for trunk-based development. You want to run all your tests in the pipeline for any new versions of the application to improve the quality. What should you do?

   • A1. Install a Git hook to require developers to run unit tests before pushing the code to a central repository. 2. Trigger Cloud Build to build the application container. Deploy the application container to a testing environment, and run integration tests. 3. If the integration tests are successful, deploy the application container to your production environment, and run acceptance tests.
   • B1. Install a Git hook to require developers to run unit tests before pushing the code to a central repository. If all tests are successful, build a container. 2. Trigger Cloud Build to deploy the application container to a testing environment, and run integration tests and acceptance tests. 3. If all tests are successful, tag the code as production ready. Trigger Cloud Build to build and deploy the application container to the production environment.
   • C1. Trigger Cloud Build to build the application container, and run unit tests with the container. 2. If unit tests are successful, deploy the application container to a testing environment, and run integration tests. 3. If the integration tests are successful, the pipeline deploys the application container to the production environment. After that, run acceptance tests.
   • D1. Trigger Cloud Build to run unit tests when the code is pushed. If all unit tests are successful, build and push the application container to a central registry. 2. Trigger Cloud Build to deploy the container to a testing environment, and run integration tests and acceptance tests. 3. If all tests are successful, the pipeline deploys the application to the production environment and runs smoke tests

   Show answer

   Answer: D

   Explanation

   The optimal approach for establishing a CI/CD pipeline for a containerized application in Google Cloud is (Option D). In this method, unit tests are seamlessly integrated into the initial stages of the pipeline, automatically triggered when code is pushed. Successful unit tests then prompt Cloud Build to construct and push the application container to a central registry. The pipeline subsequently orchestrates the deployment of the container to a testing environment, where comprehensive integration and acceptance tests are executed. Only when all tests pass successfully does the pipeline advance to deploying the application to the production environment, accompanied by the execution of smoke tests. This systematic and thorough process ensures that any code changes undergo rigorous testing at various stages, ensuring high-quality standards and instilling confidence in the deployment process to production.

   Source: Adapted from community discussion — xhilmi

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6. Question 196 · 210 in bank

   You recently created a Cloud Build pipeline for deploying Terraform code stored in a GitHub repository. You make Terraform code changes in short-lived branches and sometimes use tags during development. You tag releases with a semantic version when they are ready for deployment. You require your pipeline to apply the Terraform code whenever there is a new release, and you need to minimize operational overhead. What should you do?

   • ACreate a build trigger with the \d+\.\d+\.\d+ tag pattern.
   • BCreate a build trigger with the \d+\.\d+\.\d+ branch pattern.
   • CCreate a build trigger with the .* tag pattern.
   • DCreate a build trigger with the .* branch pattern.

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   Answer: A

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7. Question 197 · 210 in bank

   You manage a microservice that provides a public-facing API (Service A). Service A is time-critical and has a response SLO of 500 ms. Service A makes synchronous calls to internal API (Service B) that is known to become unreliable under heavy load, resulting in connection timeout errors or 500 errors. Service B is used to collect request information for the transactions processed by Service A. You need to mitigate the impact of Service B issues on your Service A users. What should you do?

   • AIncrease the size of the queue in front of the thread pool used by Service A instances.
   • BImplement retry logic with exponential back-offs when calling Service B.
   • CImplement a circuit breaker to store the request data in a database.
   • DImplement rate limiting in Service A to limit the number of requests to Service B.

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   Answer: C

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8. Question 198 · 210 in bank

   Select 2

   Your company is migrating its production systems to Google Cloud. You need to implement site reliability engineering (SRE) practices during the migration to minimize customer impact from potential future incidents. Which two SRE practices should you implement? (Choose two.)

   • ACreate up-to-date playbooks with instructions for debugging and mitigating issues.
   • BEnsure that all teams can modify the production environment to resolve issues.
   • CCreate an alerting mechanism for your SRE team based on your system's internal behavior.
   • DAutomate common tasks to analyze key impact information and intelligently suggest mitigating actions for the on-call team.
   • EEnsure that full autonomy and permissions are only granted to the on-call team.

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   Answer: AD

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9. Question 199 · 210 in bank

   You are deploying a new web application on Cloud Run in your Google Cloud project. You expect traffic to range from 10 requests per second during off-peak hours to 1000 requests per second during peak hours. You want to use autoscaling to efficiently handle the changes in traffic while ensuring that the autoscaler does not exceed your project's resource quotas. What should you do?

   • AManually adjust the number of instances based on observed traffic patterns throughout the day.
   • BDefine appropriate resource limits for the Cloud Run service, and ensure your project has sufficient resource quotas to accommodate the desired scaling range.
   • CConfigure the autoscaler to scale based on CPU utilization with a target of 80%.
   • DConfigure the autoscaler to scale based on request count, with a target of 500 requests per instance.

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   Answer: B

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10. Question 200 · 210 in bank

    Select 2

    Your company is actively developing a new web service following site reliability engineering (SRE) practices. You are leading the company's SRE team and preparing the service for production. You must ensure that the team follows Google-recommended SRE practices during this productionalization process. Which two actions should you take next? (Choose two.)

    • AEstablish monitoring and alerting systems.
    • BStart to document infrastructure system guidelines.
    • CCollaborate with the product team to design the service.
    • DImplement redundancy measures.
    • EProvide early engagement consulting to discuss architecture and design choices in detail.

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    Answer: AE

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PCDOE practice, question 191. 210 questions available.