Google Professional Cloud Architect Practice Exams
Last updated on Oct 21,2026- Exam Code: Professional Cloud Architect
- Exam Name: Google Certified Professional – Cloud Architect (GCP)
- Certification Provider: Google
- Latest update: Oct 21,2026
Your company is building a new architecture to support its data-centric business focus. You are responsible for setting up the network. Your company’s mobile and web-facing applications will be deployed on-premises, and all data analysis will be conducted in GCP. The plan is to process and load 7 years of archived .csv files totaling 900 TB of data and then continue loading 10 TB of data daily. You currently have an existing 100-MB internet connection.
What actions will meet your company’s needs?
- A . Compress and upload both achieved files and files uploaded daily using the qsutil Cm option.
- B . Lease a Transfer Appliance, upload archived files to it, and send it, and send it to Google to transfer archived data to Cloud Storage. Establish a connection with Google using a Dedicated Interconnect or Direct Peering connection and use it to upload files daily.
- C . Lease a Transfer Appliance, upload archived files to it, and send it, and send it to Google to transfer archived data to Cloud Storage. Establish one Cloud VPN Tunnel to VPC networks over the public internet, and compares and upload files daily using the gsutil Cm option.
- D . Lease a Transfer Appliance, upload archived files to it, and send it to Google to transfer archived data to Cloud Storage. Establish a Cloud VPN Tunnel to VPC networks over the public internet, and compress and upload files daily.
B
Explanation:
https://cloud.google.com/interconnect/docs/how-to/direct-peering
Your company is building a new architecture to support its data-centric business focus. You are responsible for setting up the network. Your company’s mobile and web-facing applications will be deployed on-premises, and all data analysis will be conducted in GCP. The plan is to process and load 7 years of archived .csv files totaling 900 TB of data and then continue loading 10 TB of data daily. You currently have an existing 100-MB internet connection.
What actions will meet your company’s needs?
- A . Compress and upload both achieved files and files uploaded daily using the qsutil Cm option.
- B . Lease a Transfer Appliance, upload archived files to it, and send it, and send it to Google to transfer archived data to Cloud Storage. Establish a connection with Google using a Dedicated Interconnect or Direct Peering connection and use it to upload files daily.
- C . Lease a Transfer Appliance, upload archived files to it, and send it, and send it to Google to transfer archived data to Cloud Storage. Establish one Cloud VPN Tunnel to VPC networks over the public internet, and compares and upload files daily using the gsutil Cm option.
- D . Lease a Transfer Appliance, upload archived files to it, and send it to Google to transfer archived data to Cloud Storage. Establish a Cloud VPN Tunnel to VPC networks over the public internet, and compress and upload files daily.
B
Explanation:
https://cloud.google.com/interconnect/docs/how-to/direct-peering
Your company is building a new architecture to support its data-centric business focus. You are responsible for setting up the network. Your company’s mobile and web-facing applications will be deployed on-premises, and all data analysis will be conducted in GCP. The plan is to process and load 7 years of archived .csv files totaling 900 TB of data and then continue loading 10 TB of data daily. You currently have an existing 100-MB internet connection.
What actions will meet your company’s needs?
- A . Compress and upload both achieved files and files uploaded daily using the qsutil Cm option.
- B . Lease a Transfer Appliance, upload archived files to it, and send it, and send it to Google to transfer archived data to Cloud Storage. Establish a connection with Google using a Dedicated Interconnect or Direct Peering connection and use it to upload files daily.
- C . Lease a Transfer Appliance, upload archived files to it, and send it, and send it to Google to transfer archived data to Cloud Storage. Establish one Cloud VPN Tunnel to VPC networks over the public internet, and compares and upload files daily using the gsutil Cm option.
- D . Lease a Transfer Appliance, upload archived files to it, and send it to Google to transfer archived data to Cloud Storage. Establish a Cloud VPN Tunnel to VPC networks over the public internet, and compress and upload files daily.
B
Explanation:
https://cloud.google.com/interconnect/docs/how-to/direct-peering
You are running a cluster on Kubernetes Engine to serve a web application. Users are reporting that a specific part of the application is not responding anymore. You notice that all pods of your deployment keep restarting after 2 seconds. The application writes logs to standard output. You want to inspect the logs to find the cause of the issue.
Which approach can you take?
- A . Review the Stackdriver logs for each Compute Engine instance that is serving as a node in the cluster.
- B . Review the Stackdriver logs for the specific Kubernetes Engine container that is serving the unresponsive part of the application.
- C . Connect to the cluster using gcloud credentials and connect to a container in one of the pods to read the logs.
- D . Review the Serial Port logs for each Compute Engine instance that is serving as a node in the cluster.
You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application.
Which set of steps should you take?
- A . Perform the following:
1) Create a managed instance group with f1-micro type machines.
2) Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
3) Restart the instances to automatically deploy new production releases. - B . Perform the following:
1) Create a managed instance group with n1-standard-1 type machines.
2) Build a Compute Engine image from the production branch that contains all of the dependencies and
automatically starts the Python app.
3) Rebuild the Compute Engine image, and update the instance template to deploy new production
releases. - C . Perform the following:
1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.
2) Build a Docker image from the production branch with all of the dependencies, and tag it with the version number.
3) Create a Kubernetes Deployment with the imagePullPolicy set to “IfNotPresent” in the staging namespace, and then promote it to the production namespace after testing. - D . Perform the following:
1) Create a Kubernetes Engine (GKE) cluster with n1-standard-4 type machines.
2) Build a Docker image from the master branch will all of the dependencies, and tag it with “latest”.
3) Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to “Always”.
Restart the pods to automatically deploy new production releases.
D
Explanation:
https://cloud.google.com/compute/docs/instance-templates
You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application.
Which set of steps should you take?
- A . Perform the following:
1) Create a managed instance group with f1-micro type machines.
2) Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
3) Restart the instances to automatically deploy new production releases. - B . Perform the following:
1) Create a managed instance group with n1-standard-1 type machines.
2) Build a Compute Engine image from the production branch that contains all of the dependencies and
automatically starts the Python app.
3) Rebuild the Compute Engine image, and update the instance template to deploy new production
releases. - C . Perform the following:
1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.
2) Build a Docker image from the production branch with all of the dependencies, and tag it with the version number.
3) Create a Kubernetes Deployment with the imagePullPolicy set to “IfNotPresent” in the staging namespace, and then promote it to the production namespace after testing. - D . Perform the following:
1) Create a Kubernetes Engine (GKE) cluster with n1-standard-4 type machines.
2) Build a Docker image from the master branch will all of the dependencies, and tag it with “latest”.
3) Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to “Always”.
Restart the pods to automatically deploy new production releases.
D
Explanation:
https://cloud.google.com/compute/docs/instance-templates
You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application.
Which set of steps should you take?
- A . Perform the following:
1) Create a managed instance group with f1-micro type machines.
2) Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
3) Restart the instances to automatically deploy new production releases. - B . Perform the following:
1) Create a managed instance group with n1-standard-1 type machines.
2) Build a Compute Engine image from the production branch that contains all of the dependencies and
automatically starts the Python app.
3) Rebuild the Compute Engine image, and update the instance template to deploy new production
releases. - C . Perform the following:
1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.
2) Build a Docker image from the production branch with all of the dependencies, and tag it with the version number.
3) Create a Kubernetes Deployment with the imagePullPolicy set to “IfNotPresent” in the staging namespace, and then promote it to the production namespace after testing. - D . Perform the following:
1) Create a Kubernetes Engine (GKE) cluster with n1-standard-4 type machines.
2) Build a Docker image from the master branch will all of the dependencies, and tag it with “latest”.
3) Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to “Always”.
Restart the pods to automatically deploy new production releases.
D
Explanation:
https://cloud.google.com/compute/docs/instance-templates
You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application.
Which set of steps should you take?
- A . Perform the following:
1) Create a managed instance group with f1-micro type machines.
2) Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
3) Restart the instances to automatically deploy new production releases. - B . Perform the following:
1) Create a managed instance group with n1-standard-1 type machines.
2) Build a Compute Engine image from the production branch that contains all of the dependencies and
automatically starts the Python app.
3) Rebuild the Compute Engine image, and update the instance template to deploy new production
releases. - C . Perform the following:
1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.
2) Build a Docker image from the production branch with all of the dependencies, and tag it with the version number.
3) Create a Kubernetes Deployment with the imagePullPolicy set to “IfNotPresent” in the staging namespace, and then promote it to the production namespace after testing. - D . Perform the following:
1) Create a Kubernetes Engine (GKE) cluster with n1-standard-4 type machines.
2) Build a Docker image from the master branch will all of the dependencies, and tag it with “latest”.
3) Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to “Always”.
Restart the pods to automatically deploy new production releases.
D
Explanation:
https://cloud.google.com/compute/docs/instance-templates
You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application.
Which set of steps should you take?
- A . Perform the following:
1) Create a managed instance group with f1-micro type machines.
2) Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
3) Restart the instances to automatically deploy new production releases. - B . Perform the following:
1) Create a managed instance group with n1-standard-1 type machines.
2) Build a Compute Engine image from the production branch that contains all of the dependencies and
automatically starts the Python app.
3) Rebuild the Compute Engine image, and update the instance template to deploy new production
releases. - C . Perform the following:
1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.
2) Build a Docker image from the production branch with all of the dependencies, and tag it with the version number.
3) Create a Kubernetes Deployment with the imagePullPolicy set to “IfNotPresent” in the staging namespace, and then promote it to the production namespace after testing. - D . Perform the following:
1) Create a Kubernetes Engine (GKE) cluster with n1-standard-4 type machines.
2) Build a Docker image from the master branch will all of the dependencies, and tag it with “latest”.
3) Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to “Always”.
Restart the pods to automatically deploy new production releases.
D
Explanation:
https://cloud.google.com/compute/docs/instance-templates
Use only fully managed services
CEO Statement
Our last successful game did not scale well with our previous cloud provider, resulting in lower user adoption and affecting the game’s reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the game to target users.
CTO Statement
Our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.
CFO Statement
We are not capturing enough user demographic data, usage metrics, and other KPIs. As a result, we do not engage the right users, we are not confident that our marketing is targeting the right users, and we are not selling enough premium Blast-Ups inside the games, which dramatically impacts our revenue.
For this question, refer to the Mountkirk Games case study.
Mountkirk Games wants you to design their new testing strategy.
How should the test coverage differ from their existing backends on the other platforms?
- A . Tests should scale well beyond the prior approaches.
- B . Unit tests are no longer required, only end-to-end tests.
- C . Tests should be applied after the release is in the production environment.
- D . Tests should include directly testing the Google Cloud Platform (GCP) infrastructure.
A
Explanation:
From Scenario:
A few of their games were more popular than expected, and they had problems scaling their application servers, MySQL databases, and analytics tools.
Requirements for Game Analytics Platform include: Dynamically scale up or down based on game activity