Free PROFESSIONAL CLOUD DEVOPS ENGINEER Exam Braindumps
Professional Cloud DevOps Engineer Questions 97–104 (page: 14)

Updated On: 4-Jun-2026

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?

  1. Create a single aggregated log sink at the organization level.
  2. Create a log sink in each project.
  3. Create two aggregated log sinks at the organization level, and filter by project ID.
  4. Create an aggregated log sink in the Dev and Prod folders.

Answer(s): D



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?

  1. Configure Prometheus cross-service federation for centralized data access.
  2. Configure workload metrics within Cloud Operations for GKE.
  3. Configure Prometheus hierarchical federation for centralized data access.
  4. Configure Google Cloud Managed Service for Prometheus.

Answer(s): D



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?

  1. 1. 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.
  2. 1. 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.
  3. 1. 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.
  4. 1. 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

Answer(s): D



The new version of your containerized application has been tested and is ready to be deployed to production on Google Kubernetes Engine (GKE). You could not fully load-test the new version in your pre-production environment, and you need to ensure that the application does not have performance problems after deployment. Your deployment must be automated. What should you do?

  1. Deploy the application through a continuous delivery pipeline by using canary deployments. Use Cloud
    Monitoring to look for performance issues, and ramp up traffic as supported by the metrics.
  2. Deploy the application through a continuous delivery pipeline by using blue/green deployments. Migrate traffic to the new version of the application and use Cloud Monitoring to look for performance issues.
  3. Deploy the application by using kubectl and use Config Connector to slowly ramp up traffic between versions. Use Cloud Monitoring to look for performance issues.
  4. Deploy the application by using kubectl and set the spec.updateStrategy.type field to RollingUpdate. Use Cloud Monitoring to look for performance issues, and run the kubectl rollback command if there are any issues.

Answer(s): A



You are managing an application that runs in Compute Engine. The application uses a custom HTTP server to expose an API that is accessed by other applications through an internal TCP/UDP load balancer. A firewall rule allows access to the API port from 0.0.0.0/0. You need to configure Cloud Logging to log each IP address that accesses the API by using the fewest number of steps. What should you do first?

  1. Enable Packet Mirroring on the VPC.
  2. Install the Ops Agent on the Compute Engine instances.
  3. Enable logging on the firewall rule.
  4. Enable VPC Flow Logs on the subnet.

Answer(s): D



Your company runs an ecommerce website built with JVM-based applications and microservice architecture in Google Kubernetes Engine (GKE). The application load increases during the day and decreases during the night. Your operations team has configured the application to run enough Pods to handle the evening peak load. You want to automate scaling by only running enough Pods and nodes for the load. What should you do?

  1. Configure the Vertical Pod Autoscaler, but keep the node pool size static.
  2. Configure the Vertical Pod Autoscaler, and enable the cluster autoscaler.
  3. Configure the Horizontal Pod Autoscaler, but keep the node pool size static.
  4. Configure the Horizontal Pod Autoscaler, and enable the cluster autoscaler.

Answer(s): D



Your organization wants to increase the availability target of an application from 99.9% to 99.99% for an investment of $2,000. The application's current revenue is $1,000,000. You need to determine whether the increase in availability is worth the investment for a single year of usage. What should you do?

  1. Calculate the value of improved availability to be $900, and determine that the increase in availability is not worth the investment.
  2. Calculate the value of improved availability to be $1,000, and determine that the increase in availability is not worth the investment.
  3. Calculate the value of improved availability to be $1,000, and determine that the increase in availability is worth the investment.
  4. Calculate the value of improved availability to be $9,000, and determine that the increase in availability is worth the investment.

Answer(s): A



A third-party application needs to have a service account key to work properly. When you try to export the key from your cloud project, you receive an error: "The organization policy constraint iam.disableServiceAccounKeyCreation is enforced." You need to make the third-party application work while following Google-recommended security practices.

What should you do?

  1. Enable the default service account key, and download the key.
  2. Remove the iam.disableServiceAccountKeyCreation policy at the organization level, and create a key.
  3. Disable the service account key creation policy at the project's folder, and download the default key.
  4. Add a rule to set the iam.disableServiceAccountKeyCreation policy to off in your project, and create a key.

Answer(s): D



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