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Google Professional-Cloud-Architect Certification Exam is a highly sought-after certification for professionals seeking to demonstrate their expertise in designing, developing, and managing solutions on Google Cloud Platform (GCP). Professional-Cloud-Architect exam is designed to test the candidate's knowledge and skills in various areas of GCP, including security, networking, storage, compute, and application development. Google Certified Professional - Cloud Architect (GCP) certification is intended for cloud architects, solution architects, and technical leads who are responsible for designing and implementing GCP solutions for their organizations.

Google Professional-Cloud-Architect Certification is aimed at professionals who have experience in designing and managing cloud-based solutions. Google Certified Professional - Cloud Architect (GCP) certification is a valuable asset for individuals who want to advance their careers in cloud architecture. Google Certified Professional - Cloud Architect (GCP) certification demonstrates the candidate’s ability to design and implement cloud solutions using GCP services.

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Then, candidates can use different books, like the following:

Professional Cloud Architect – Google Cloud Certification Guide written by Konrad Clapa & Brian Gerrard
The Ultimate Guide for Beginners [Google Cloud Platform] by Adney Ainsley
Google Cloud Certified Professional Cloud Architect (3rd Edition) by Soumen Chatterjee

All of them are available on Amazon and throw light on major Google Cloud concepts. They allow one to not just learn all the topics but also understand how to solve real-world tasks related to the implementation of GCP. Various scenarios explained and questions for self-assessment are included too.

To familiarize the applicants with daily issues the professionals face in the workplace, the vendor has provided some detailed case studies to refer to. These relate to Mountkirk Games, Dress4Win, and TerramEarth. Finally, one can join an on-demand webinar “Architecting Hybrid Cloud Infrastructure with Anthos” that provides valuable exam tips.

Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q334-Q339):

NEW QUESTION # 334
You deploy your custom java application to google app engine.
It fails to deploy and gives you the following stack trace:

A. Digitally sign all of your JAR files and redeploy your application.
B. Upload missing JAR files and redeploy your application
C. Recompile the CLoakedServlet class using and MD5 hash instead of SHA1

Answer: A

NEW QUESTION # 335
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 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.
B. 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.
C. 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.
D. 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.

Answer: D

NEW QUESTION # 336
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. Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.
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. 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.
D. Begin packaging their game backend artifacts in container images and running them on Kubernetes Engine to improve the availability to scale up or down based on game activity.
E. Adopt a schema versioning tool to reduce downtime when adding new game features that require storing additional player data in the database.

Answer: A,B

Explanation:
Explanation/Reference:

NEW QUESTION # 337
You are working with a data warehousing team that performs data analysis. The team needs to process data from external partners, but the data contains personally identifiable information (PII).
You need to process and store the data without storing any of the PII data. What should you do?

A. Create a Dataflow pipeline to retrieve the data from the external sources. As part of the pipeline, use the Cloud Data Loss Prevention (Cloud DLP) API to remove any PII data. Store the result in BigQuery.
B. Ask the external partners to import all data in your BigQuery dataset. Create a dataflow pipeline to copy the data into a new table. As part of the Dataflow bucket, skip all data in columns that have PII data
C. Ask the external partners to upload all data on Cloud Storage. Configure Bucket Lock for the bucket. Create a Dataflow pipeline to read the data from the bucket. As part of the pipeline, use the Cloud Data Loss Prevention (Cloud DLP) API to remove any PII data. Store the result in BigQuery.
D. Create a Dataflow pipeline to retrieve the data from the external sources. As part of the pipeline, store all non-PII data in BigQuery and store all PII data in a Cloud Storage bucket that has a retention policy set.

Answer: A

Explanation:
In addition to storing personal data in the GCS, it is being improperly retained.

NEW QUESTION # 338
Your organization uses separate Google Cloud projects for shared services, development, testing, and production.
- The shared services project hosts your private CI/CD runners and a
central Artifact Registry
- The development, testing, and production projects host the GKE
clusters where applications are deployed.
You need to design an architecture that allows the CI/CD runners to connect to the GKE clusters and the clusters to pull images from Artifact Registry, all using private IP addresses. However, direct network traffic between the development, testing, and production environments must be strictly prohibited. What should you do?

A. Create a separate VPC in each of the four projects. Connect each environment's VPC to the shared services VPC through VPC Network Peering.
B. Create a separate VPC in each project. Use VPC Network Peering to create a full mesh, connecting every VPC directly to every other VPC.
C. Expose the resources in the shared services project using an external load balancer. Implement a firewall rule to limit access.
D. Configure the shared services project as a Shared VPC host. Create a single VPC in this host project and attach the environment projects as service projects.

Answer: A

Explanation:
Creating separate VPCs for shared services, development, testing, and production, and then peering only each environment VPC with the shared services VPC forms a hub-and-spoke design. This allows:
- CI/CD runners (in shared services VPC) to reach each GKE cluster over private IP.
- GKE clusters (in env VPCs) to pull images privately from Artifact Registry (via Private Service Connect / VPC-SC aware patterns).
- No direct connectivity between dev, test, and prod, because VPC Network Peering is non- transitive and you do not peer the environment VPCs with each other, thus enforcing strong isolation by design.

NEW QUESTION # 339
......

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