Google Professional Cloud Architect Practice Exams
Last updated on Oct 23,2026- Exam Code: Professional Cloud Architect
- Exam Name: Google Certified Professional – Cloud Architect (GCP)
- Certification Provider: Google
- Latest update: Oct 23,2026
Your company is developing a web-based application. You need to make sure that production deployments are linked to source code commits and are fully auditable.
What should you do?
- A . Make sure a developer is tagging the code commit with the date and time of commit
- B . Make sure a developer is adding a comment to the commit that links to the deployment.
- C . Make the container tag match the source code commit hash.
- D . Make sure the developer is tagging the commits with: latest
C
Explanation:
From: https://cloud.google.com/architecture/best-practices-for-building-containers
Under: Tagging using the Git commit hash (bottom of page almost)
"In this case, a common way of handling version numbers is to use the Git commit SHA-1 hash (or a short version of it) as the version number. By design, the Git commit hash is immutable and references a specific version of your software.
You can use this commit hash as a version number for your software, but also as a tag for the Docker image built from this specific version of your software. Doing so makes Docker images traceable: because in this case the image tag is immutable, you instantly know which specific version of your software is running inside a given container."
You are creating a solution to remove backup files older than 90 days from your backup Cloud Storage bucket. You want to optimize ongoing Cloud Storage spend.
What should you do?
- A . Write a lifecycle management rule in XML and push it to the bucket with gsutil.
- B . Write a lifecycle management rule in JSON and push it to the bucket with gsutil.
- C . Schedule a cron script using gsutil is -lr gs://backups/** to find and remove items older than 90 days.
- D . Schedule a cron script using gsutil ls -1 gs://backups/** to find and remove items older than 90 days and schedule it with cron.
B
Explanation:
https://cloud.google.com/storage/docs/gsutil/commands/lifecycle
Your application needs to process credit card transactions. You want the smallest scope of Payment Card Industry (PCI) compliance without compromising the ability to analyze transactional data and trends relating to which payment methods are used.
How should you design your architecture?
- A . Create a tokenizer service and store only tokenized data.
- B . Create separate projects that only process credit card data.
- C . Create separate subnetworks and isolate the components that process credit card data.
- D . Streamline the audit discovery phase by labeling all of the virtual machines (VMs) that process PCI data.
- E . Enable Logging export to Google BigQuery and use ACLs and views to scope the data shared with the auditor.
A
Explanation:
https://cloud.google.com/solutions/pci-dss-compliance-in-gcp
You are building a continuous deployment pipeline for a project stored in a Git source repository and want to ensure that code changes can be verified deploying to production.
What should you do?
- A . Use Spinnaker to deploy builds to production using the red/black deployment strategy so that changes can easily be rolled back.
- B . Use Spinnaker to deploy builds to production and run tests on production deployments.
- C . Use Jenkins to build the staging branches and the master branch. Build and deploy changes to production for 10% of users before doing a complete rollout.
- D . Use Jenkins to monitor tags in the repository. Deploy staging tags to a staging environment for testing.
After testing, tag the repository for production and deploy that to the production environment.
D
Explanation:
Reference: https://github.com/GoogleCloudPlatform/continuous-deployment-on-kubernetes/blob/master/README.md
You are building a continuous deployment pipeline for a project stored in a Git source repository and want to ensure that code changes can be verified deploying to production.
What should you do?
- A . Use Spinnaker to deploy builds to production using the red/black deployment strategy so that changes can easily be rolled back.
- B . Use Spinnaker to deploy builds to production and run tests on production deployments.
- C . Use Jenkins to build the staging branches and the master branch. Build and deploy changes to production for 10% of users before doing a complete rollout.
- D . Use Jenkins to monitor tags in the repository. Deploy staging tags to a staging environment for testing.
After testing, tag the repository for production and deploy that to the production environment.
D
Explanation:
Reference: https://github.com/GoogleCloudPlatform/continuous-deployment-on-kubernetes/blob/master/README.md
For this question, refer to the Dress4Win case study. You want to ensure that your on-premises architecture meets business requirements before you migrate your solution.
What change in the on-premises architecture should you make?
- A . Replace RabbitMQ with Google Pub/Sub.
- B . Downgrade MySQL to v5.7, which is supported by Cloud SQL for MySQL.
- C . Resize compute resources to match predefined Compute Engine machine types.
- D . Containerize the micro services and host them in Google Kubernetes Engine.
Create an Object Lifecycle rule to move files into a Coldline Cloud Storage bucket after one month.
Explanation:
The practice for managing logs generated on Compute Engine on Google Cloud is to install the Cloud Logging agent and send them to Cloud Logging.
The sent logs will be aggregated into a Cloud Logging sink and exported to Cloud Storage.
The reason for using Cloud Storage as the destination for the logs is that the requirement in question requires setting up a lifecycle based on the storage period.
In this case, the log will be used for active queries for 30 days after it is saved, but after that, it needs to be stored for a longer period of time for auditing purposes.
If the data is to be used for active queries, we can use BigQuery’s Cloud Storage data query feature and move the data past 30 days to Coldline to build a cost-optimal solution.
Therefore, the correct answer is as follows
Create an Object Lifecycle rule to move files into a Coldline Cloud Storage bucket after one month.
Explanation:
The practice for managing logs generated on Compute Engine on Google Cloud is to install the Cloud Logging agent and send them to Cloud Logging.
The sent logs will be aggregated into a Cloud Logging sink and exported to Cloud Storage.
The reason for using Cloud Storage as the destination for the logs is that the requirement in question requires setting up a lifecycle based on the storage period.
In this case, the log will be used for active queries for 30 days after it is saved, but after that, it needs to be stored for a longer period of time for auditing purposes.
If the data is to be used for active queries, we can use BigQuery’s Cloud Storage data query feature and move the data past 30 days to Coldline to build a cost-optimal solution.
Therefore, the correct answer is as follows
For this question, refer to the Dress4Win case study.
As part of their new application experience, Dress4Wm allows customers to upload images of themselves. The customer has exclusive control over who may view these images. Customers should be able to upload images with minimal latency and also be shown their images quickly on the main application page when they log in.
Which configuration should Dress4Win use?
- A . Store image files in a Google Cloud Storage bucket. Use Google Cloud Datastore to maintain metadata that maps each customer’s ID and their image files.
- B . Store image files in a Google Cloud Storage bucket. Add custom metadata to the uploaded images in Cloud Storage that contains the customer’s unique ID.
- C . Use a distributed file system to store customers’ images. As storage needs increase, add more persistent disks and/or nodes. Assign each customer a unique ID, which sets each file’s owner attribute, ensuring privacy of images.
- D . Use a distributed file system to store customers’ images. As storage needs increase, add more persistent disks and/or nodes. Use a Google Cloud SQL database to maintain metadata that maps each customer’s ID to their image files.
Your company has an application running on a deployment in a GKE cluster. You have a separate cluster for development, staging and production. You have discovered that the team is able to deploy a Docker image to the production cluster without first testing the deployment in development and then staging. You want to allow the team to have autonomy but want to prevent this from happening. You want a Google Cloud solution that can be implemented quickly with minimal effort.
What should you do?
- A . Create a Kubernetes admission controller to prevent the container from starting if it is not approved for usage in the given environment
- B . Configure a Kubernetes lifecycle hook to prevent the container from starting if it is not approved for usage in the given environment
- C . Implement a corporate policy to prevent teams from deploying Docker image to an environment unless the Docker image was tested in an earlier environment
- D . Configure the binary authorization policies for the development, staging and production clusters. Create attestations as part of the continuous integration pipeline”
D
Explanation:
https://cloud.google.com/architecture/prep-kubernetes-engine-for-prod#binary-authorization
The most common Binary Authorization use cases involve attestations. An attestation certifies that a specific image has completed a previous stage, as described previously. You configure the Binary Authorization policy to verify the attestation before allowing the image to be deployed. At deploy time, instead of redoing activities that were completed in earlier stages, Binary Authorization only needs to verify the attestation. https://cloud.google.com/binary-authorization/docs/overview