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Professional-Cloud-DevOps-Engineer New Exam Materials | Professional-Cloud-DevOps-Engineer Practice Exam Online

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Test Structure

The candidates who want to take this Google exam will have two hours to answer all questions. Even though the vendor doesn’t give details on the total number of questions that the examinees will receive, they should be prepared to solve multiple-choice and multiple-answer inquiries. Besides, the test is delivered in the English language only. As for the registration fee, the test-takers will need to pay $200 to take it. Additional taxes may apply depending on the candidate’s profile and chosen delivery method. By and large, the applicants have two options to take the official exam. They can choose to take it online from any remote location that they prefer. If they choose this option, the candidates should read carefully what the testing requirements are. In case applicants prefer to be present in a classroom when they take the actual testing, then they can search for a test center that is closest to their location. Also, Google doesn’t have any prerequisites for the candidates to be eligible for the evaluation. Still, it recommends that the candidates for the Professional Cloud DevOps Engineer exam should have at least 3 years of experience in the industry including a minimum of one year of experience in managing and developing solutions on GCP.

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The Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam consists of multiple-choice and scenario-based questions that test the candidate's practical experience and knowledge of DevOps practices on the Google Cloud Platform. Professional-Cloud-DevOps-Engineer Exam is available in several languages, including English, Japanese, and Spanish, and is delivered through a secure online platform.

What is the duration, language, and format of Google Professional Cloud DevOps Engineer Exam

Language: English
Passing score: 80%
Length of Examination: 120 minutes
Number of Questions: 50-60
Format: Multiple choices, multiple select

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
You recently deployed your application in Google Kubernetes Engine (GKE) and now need to release a new version of the application You need the ability to instantly roll back to the previous version of the application in case there are issues with the new version Which deployment model should you use?

A. Perform a blue/green deployment and test your new application after the deployment is complete
B. Perform a rolling deployment and test your new application after the deployment is complete
C. Perform a canary deployment, and test your new application periodically after the new version is deployed
D. Perform A. B testing, and test your application periodically after the deployment is complete

Answer: A

Explanation:
The best deployment model for releasing a new version of your application in GKE with the ability to instantly roll back to the previous version is to perform a blue/green deployment and test your new application after the deployment is complete. A blue/green deployment is a deployment strategy that involves creating two identical environments, one running the current version of the application (blue) and one running the new version of the application (green). The traffic is switched from blue to green after testing the new version, and if any issues are discovered, the traffic can be switched back to blue instantly. This way, you can minimize downtime and risk during deployment.

NEW QUESTION # 84
You are designing a system with three different environments: development, quality assurance (QA), and production.
Each environment will be deployed with Terraform and has a Google Kubemetes Engine (GKE) cluster created so that application teams can deploy their applications. Anthos Config Management will be used and templated to deploy infrastructure level resources in each GKE cluster. All users (for example, infrastructure operators and application owners) will use GitOps. How should you structure your source control repositories for both Infrastructure as Code (laC) and application code?

A. Cloud Infrastructure (Terraform) repository is shared: different branches are different environmentsGKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: differentoverlay directories are different environmentsApplication (app source code) repository is shared: different directories are different features
B. Cloud Infrastructure (Terraform) repository is shared: different directories are different environmentsGKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated:different branches are different environmentsApplication (app source code) repositories are separated: different branches are different features
C. Cloud Infrastructure (Terraform) repositories are separated: different branches are different environmentsGKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated:different overlay directories are different environmentsApplication (app source code) repositories are separated: different branches are different features
D. Cloud Infrastructure (Terraform) repository is shared: different directories are different environmentsGKE Infrastructure (Anthos Config Management Kustomize manifests) repository is shared: differentoverlay directories are different environmentsApplication (app source code) repositories are separated: different branches are different features

Answer: B

NEW QUESTION # 85
Your organization is using Helm to package containerized applications Your applications reference both public and private charts Your security team flagged that using a public Helm repository as a dependency is a risk You want to manage all charts uniformly, with native access control and VPC Service Controls What should you do?

A. Configure a Helm chart repository server to run in Google Kubernetes Engine (GKE) with Cloud Storage bucket as the storage backend
B. Store public and private charts in OCI format by using Artifact Registry
C. Store public and private charts by using GitHub Enterprise with Google Workspace as the identity provider
D. Store public and private charts by using Git repository Configure Cloud Build to synchronize contents of the repository into a Cloud Storage bucket Connect Helm to the bucket by using https: // [bucket] .srorage.googleapis.com/ [holnchart] as the Helm repository

Answer: B

Explanation:
The best option for managing all charts uniformly, with native access control and VPC Service Controls is to store public and private charts in OCI format by using Artifact Registry. Artifact Registry is a service that allows you to store and manage container images and other artifacts in Google Cloud. Artifact Registry supports OCI format, which is an open standard for storing container images and other artifacts such as Helm charts. You can use Artifact Registry to store public and private charts in OCI format and manage them uniformly. You can also use Artifact Registry's native access control features, such as IAM policies and VPC Service Controls, to secure your charts and control who can access them.

NEW QUESTION # 86
You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects Log entries for development projects must be exported to dev_dataset. and log entries for production projects must be exported to prod_dataset You need to minimize the number of log sinks created and you want to ensure that the log sinks apply to future projects What should you do?

A. Create two aggregated log sinks at the organization level, and filter by project ID
B. Create a log sink in each project
C. Create an aggregated Iog sink in the Dev and Prod folders
D. Create a single aggregated log sink at the organization level.

Answer: C

Explanation:
Explanation
The best option for minimizing the number of log sinks created and ensuring that the log sinks apply to future projects is to create an aggregated log sink in the Dev and Prod folders. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. By creating an aggregated log sink in each folder, you can export log entries for development projects to dev_dataset and log entries for production projects to prod_dataset. You can also use filters to specify which logs you want to export.
Additionally, by creating an aggregated log sink at the folder level, you can ensure that the log sink applies to future projects that are created under that folder.

NEW QUESTION # 87
You support a web application that is hosted on Compute Engine. The application provides a booking service for thousands of users. Shortly after the release of a new feature, your monitoring dashboard shows that all users are experiencing latency at login. You want to mitigate the impact of the incident on the users of your service. What should you do first?

A. Roll back the recent release.
B. Upsize the virtual machines running the login services.
C. Review the Stackdriver monitoring.
D. Deploy a new release to see whether it fixes the problem.

Answer: B

Explanation:
Rollback to previous stable version. Then you need to find what is causing the issue.

NEW QUESTION # 88
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