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Google Professional-Cloud-Architect Exam Overview:

Certification Vendor:	Google
Exam Name:	Google Certified Professional - Cloud Architect
Exam Number:	Professional-Cloud-Architect
Certificate Validity Period:	2 years
Passing Score:	Not publicly disclosed
Related Certifications:	Google Cloud Certified - Professional Cloud Architect
Available Languages:	Portuguese, French, German, English, Japanese, Indonesian, Spanish
Exam Format:	Multiple Select, Multiple Choice
Exam Price:	$200 USD
Exam Duration:	120 minutes
Real Exam Qty:	50-60
Sample Questions:	Google Professional-Cloud-Architect Sample Questions
Exam Way:	Online (proctored) or at a test center
Pre Condition:	No mandatory prerequisite, but recommended to have 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud
Official Syllabus URL:	https://cloud.google.com/learn/certification/cloud-architect

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Passing the Google Professional-Cloud-Architect Exam is a great way to demonstrate your expertise in using GCP to design and manage cloud solutions. With this certification, you can show potential employers that you have the skills needed to design and implement complex cloud architectures using GCP. Google Certified Professional - Cloud Architect (GCP) certification can also lead to new career opportunities and higher salaries.

Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q327-Q332):

NEW QUESTION # 327
Your company has an application that is running on multiple instances of Compute Engine. It generates 1 TB per day of logs. For compliance reasons, the logs need to be kept for at least two years. The logs need to be available for active query for 30 days. After that, they just need to be retained for audit purposes. You want to implement a storage solution that is compliant, minimizes costs, and follows Google-recommended practices.
What should you do?

A. 1. Install the Cloud Ops agent on all instances.
2. Create a sink to export logs into a partitioned BigQuery table.
3. Set a time_partitioning_expiration of 30 days.
B. 1. Write a daily cron job, running on all instances, that uploads logs into a Cloud Storage bucket.
2. Create a sink to export logs into a regional Cloud Storage bucket.
3. Create an Object Lifecycle rule to move files into a Coldline Cloud Storage bucket after one month.
C. 1. Create a daily cron job, running on all instances, that uploads logs into a partitioned BigQuery table. 2. Set a time_partitioning_expiration of 30 days.
D. 1. Install the Cloud Ops agent on all instances.
2. Create a sink to export logs into a regional Cloud Storage bucket.
3. Create an Object Lifecycle rule to move files into a Coldline Cloud Storage bucket after one month.
4. Configure a retention policy at the bucket level to create a lock.

Answer: D

Explanation:
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
1. Install the Cloud Logging agent on all instances.
Create a sync that exports the logs to the region's Cloud Storage bucket.
3. Create an Object Lifecycle rule to move the files to the Coldline Cloud Storage bucket after one month. 4.
4. set up a bucket-level retention policy using bucket locking."

NEW QUESTION # 328
Your company wants you to build a highly reliable web application with a few public APIs as the backend.
You don't expect a lot of user traffic, but traffic could spike occasionally. You want to leverage Cloud Load Balancing, and the solution must be cost-effective for users. What should you do?

A. Store static content such as HTML and images in a Cloud Storage bucket. Use Cloud Functions to host the APIs and save the user data in Firestore.
B. Store static content such as HTML and images in a Cloud Storage bucket. Host the APIs on a zonal Google Kubernetes Engine cluster with worker nodes in multiple zones, and save the user data in Cloud Spanner.
C. Store static content such as HTML and images in Cloud CDN. Host the APIs on App Engine and store the user data in Cloud SQL.
D. Store static content such as HTML and images in Cloud CDN. Use Cloud Run to host the APIs and save the user data in Cloud SQL.

Answer: A

Explanation:
https://cloud.google.com/load-balancing/docs/https/setting-up-https-serverless#gcloud:-cloud-functions
https://cloud.google.com/blog/products/networking/better-load-balancing-for-app-engine-cloud-run-and-functions

NEW QUESTION # 329
A production database virtual machine on Google Compute Engine has an ext4-formatted persistent disk for data files The database is about to run out of storage space How can you remediate the problem with the least amount of downtime?

A. In the Cloud Platform Console, increase the size of the persistent disk and use the resize2fs command in Linux.
B. In the Cloud Platform Console, create a snapshot of the persistent disk, restore the snapshot to a new larger disk, unmount the old disk, mount the new disk, and restart the database service.
C. Shut down the virtual machine, use the Cloud Platform Console to increase the persistent disk size, then restart the virtual machine.
D. In the Cloud Platform Console, create a new persistent disk attached to the virtual machine, format and mount it, and configure the database service to move the files to the new disk.
E. In the Cloud Platform Console, increase the size of the persistent disk and verify the new space is ready to use with the fdisk command in Linux.

Answer: A

NEW QUESTION # 330
For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to design their solution for the future in order to take advantage of cloud and technology improvements as they become available. Which two steps should they take? (Choose two.)

A. Adopt a schema versioning tool to reduce downtime when adding new game features that require storing additional player data in the database.
B. Implement a weekly rolling maintenance process for the Linux virtual machines so they can apply critical kernel patches and package updates and reduce the risk of 0-day vulnerabilities.
C. Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.
D. Begin packaging their game backend artifacts in container images and running them on Google Kubernetes Engine to improve the availability to scale up or down based on game activity.
E. Store as much analytics and game activity data as financially feasible today so it can be used to train machine learning models to predict user behavior in the future.

Answer: B,C

NEW QUESTION # 331
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 Kubernetes Engine 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.
C. 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.
D. 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.

Answer: D

NEW QUESTION # 332
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