Google Professional Cloud Developer Practice Exams
Last updated on Oct 24,2026- Exam Code: Professional Cloud Developer
- Exam Name: Professional Cloud Developer
- Certification Provider: Google
- Latest update: Oct 24,2026
You are using Cloud Build for your CI/CD pipeline to complete several tasks, including copying certain files to Compute Engine virtual machines. Your pipeline requires a flat file that is generated in one builder in the pipeline to be accessible by subsequent builders in the same pipeline.
How should you store the file so that all the builders in the pipeline can access it?
- A . Store and retrieve the file contents using Compute Engine instance metadata.
- B . Output the file contents to a file in /workspace. Read from the same /workspace file in the subsequent build step.
- C . Use gsutil to output the file contents to a Cloud Storage object. Read from the same object in the subsequent build step.
- D . Add a build argument that runs an HTTP POST via curl to a separate web server to persist the value in one builder. Use an HTTP GET via curl from the subsequent build step to read the value.
B
Explanation:
https://cloud.google.com/build/docs/build-config-file-schema
You recently migrated a monolithic application to Google Cloud by breaking it down into microservices. One of the microservices is deployed using Cloud Functions. As you modernize the application, you make a change to the API of the service that is backward-incompatible. You need to support both existing callers who use the original API and new callers who use the new API.
What should you do?
- A . Leave the original Cloud Function as-is and deploy a second Cloud Function with the new API. Use a load balancer to distribute calls between the versions.
- B . Leave the original Cloud Function as-is and deploy a second Cloud Function that includes only the changed API. Calls are automatically routed to the correct function.
- C . Leave the original Cloud Function as-is and deploy a second Cloud Function with the new API. Use Cloud Endpoints to provide an API gateway that exposes a versioned API.
- D . Re-deploy the Cloud Function after making code changes to support the new API. Requests for both versions of the API are fulfilled based on a version identifier included in the call.
D
Explanation:
Reference: https://cloud.google.com/endpoints/docs/openapi/versioning-an-api
For this question, refer to the HipLocal case study.
How should HipLocal increase their API development speed while continuing to provide the QA team with a stable testing environment that meets feature requirements?
- A . Include unit tests in their code, and prevent deployments to QA until all tests have a passing status.
- B . Include performance tests in their code, and prevent deployments to QA until all tests have a passing status.
- C . Create health checks for the QA environment, and redeploy the APIs at a later time if the environment is unhealthy.
- D . Redeploy the APIs to App Engine using Traffic Splitting. Do not move QA traffic to the new versions if errors are found.
Which service should HipLocal use for their public APIs?
- A . Cloud Armor
- B . Cloud Functions
- C . Cloud Endpoints
- D . Shielded Virtual Machines
You are a developer working with the CI/CD team to troubleshoot a new feature that your team introduced. The CI/CD team used HashiCorp Packer to create a new Compute Engine image from your development branch. The image was successfully built, but is not booting up. You need to investigate the issue with the CI/CD team.
What should you do?
- A . Create a new feature branch, and ask the build team to rebuild the image.
- B . Shut down the deployed virtual machine, export the disk, and then mount the disk locally to access the boot logs.
- C . Install Packer locally, build the Compute Engine image locally, and then run it in your personal Google Cloud project.
- D . Check Compute Engine OS logs using the serial port, and check the Cloud Logging logs to confirm access to the serial port.
D
Explanation:
https://cloud.google.com/compute/docs/troubleshooting/troubleshooting-using-serial-console
You need to redesign the ingestion of audit events from your authentication service to allow it to handle a large increase in traffic. Currently, the audit service and the authentication system run in the same Compute Engine virtual machine.
You plan to use the following Google Cloud tools in the new architecture:
Multiple Compute Engine machines, each running an instance of the authentication service
Multiple Compute Engine machines, each running an instance of the audit service
Pub/Sub to send the events from the authentication services.
How should you set up the topics and subscriptions to ensure that the system can handle a large volume of messages and can scale efficiently?
- A . Create one Pub/Sub topic. Create one pull subscription to allow the audit services to share the messages.
- B . Create one Pub/Sub topic. Create one pull subscription per audit service instance to allow the services to share the messages.
- C . Create one Pub/Sub topic. Create one push subscription with the endpoint pointing to a load balancer in front of the audit services.
- D . Create one Pub/Sub topic per authentication service. Create one pull subscription per topic to be used by one audit service.
- E . Create one Pub/Sub topic per authentication service. Create one push subscription per topic, with the endpoint pointing to one audit service.
A
Explanation:
https://cloud.google.com/pubsub/docs/subscriber "Multiple subscribers can make pull calls to the same "shared" subscription. Each subscriber will receive a subset of the messages."
Your company has a BigQuery dataset named "Master" that keeps information about employee travel and expenses. This information is organized by employee department. That means employees should only be able to view information for their department. You want to apply a security framework to enforce this requirement with the minimum number of steps.
What should you do?
- A . Create a separate dataset for each department. Create a view with an appropriate WHERE clause to select records from a particular dataset for the specific department. Authorize this view to access records from your Master dataset. Give employees the permission to this department-specific dataset.
- B . Create a separate dataset for each department. Create a data pipeline for each department to copy appropriate information from the Master dataset to the specific dataset for the department. Give employees the permission to this department-specific dataset.
- C . Create a dataset named Master dataset. Create a separate view for each department in the Master dataset. Give employees access to the specific view for their department.
- D . Create a dataset named Master dataset. Create a separate table for each department in the Master dataset. Give employees access to the specific table for their department.
You are using Cloud Build to create a new Docker image on each source code commit to a Cloud Source Repositoties repository. Your application is built on every commit to the master branch. You want to release specific commits made to the master branch in an automated method.
What should you do?
- A . Manually trigger the build for new releases.
- B . Create a build trigger on a Git tag pattern. Use a Git tag convention for new releases.
- C . Create a build trigger on a Git branch name pattern. Use a Git branch naming convention for new releases.
- D . Commit your source code to a second Cloud Source Repositories repository with a second Cloud Build trigger. Use this repository for new releases only.
C
Explanation:
Reference: https://docs.docker.com/docker-hub/builds/
Your company has a data warehouse that keeps your application information in BigQuery. The BigQuery data warehouse keeps 2 PBs of user data. Recently, your company expanded your user base to include EU users and needs to comply with these requirements:
Your company must be able to delete all user account information upon user request.
All EU user data must be stored in a single region specifically for EU users.
Which two actions should you take? (Choose two.)
- A . Use BigQuery federated queries to query data from Cloud Storage.
- B . Create a dataset in the EU region that will keep information about EU users only.
- C . Create a Cloud Storage bucket in the EU region to store information for EU users only.
- D . Re-upload your data using to a Cloud Dataflow pipeline by filtering your user records out.
- E . Use DML statements in BigQuery to update/delete user records based on their requests.
C,E
Explanation:
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse
You are running a containerized application on Google Kubernetes Engine. Your container images are stored in Container Registry. Your team uses CI/CD practices. You need to prevent the deployment of containers with known critical vulnerabilities.
What should you do?
- A . • Use Web Security Scanner to automatically crawl your application
• Review your application logs for scan results, and provide an attestation that the container is free of known critical vulnerabilities
• Use Binary Authorization to implement a policy that forces the attestation to be provided before the container is deployed - B . • Use Web Security Scanner to automatically crawl your application
• Review the scan results in the scan details page in the Cloud Console, and provide an attestation that the container is free of known critical vulnerabilities
• Use Binary Authorization to implement a policy that forces the attestation to be provided before the container is deployed - C . • Enable the Container Scanning API to perform vulnerability scanning
• Review vulnerability reporting in Container Registry in the Cloud Console, and provide an attestation that the container is free of known critical vulnerabilities
• Use Binary Authorization to implement a policy that forces the attestation to be provided before the container is deployed - D . • Enable the Container Scanning API to perform vulnerability scanning
• Programmatically review vulnerability reporting through the Container Scanning API, and provide an attestation that the container is free of known critical vulnerabilities
• Use Binary Authorization to implement a policy that forces the attestation to be provided before the container is deployed
D
Explanation:
https://cloud.google.com/binary-authorization/docs/creating-attestations-kritis
https://cloud.google.com/container-analysis/docs/os-overview