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Google Professional-Cloud-DevOps-Engineer Certification Exam is a highly respected certification in the field of cloud computing. It is designed to validate the skills and knowledge of professionals who are responsible for building and managing cloud-based solutions using Google Cloud Platform. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam requires a deep understanding of DevOps practices, as well as a comprehensive knowledge of Google Cloud Platform tools and services.

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Google Professional-Cloud-DevOps-Engineer Certification Exam is a comprehensive exam that covers a wide range of topics related to DevOps practices and GCP services. Professional-Cloud-DevOps-Engineer exam tests the candidate's knowledge and understanding of DevOps methodologies, including continuous integration and delivery, automation, and monitoring. It also assesses their ability to implement GCP services such as Compute Engine, App Engine, Kubernetes Engine, and Cloud Functions, among others.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q156-Q161):

NEW QUESTION # 156
You need to run a business-critical workload on a fixed set of Compute Engine instances for several months. The workload is stable with the exact amount of resources allocated to it. You want to lower the costs for this workload without any performance implications. What should you do?

A. Convert the instances to preemptible virtual machines.
B. Purchase Committed Use Discounts.
C. Migrate the instances to a Managed Instance Group.
D. Create an Unmanaged Instance Group for the instances used to run the workload.

Answer: D

NEW QUESTION # 157
Your company has recently experienced several production service issues. You need to create a Cloud Monitoring dashboard to troubleshoot the issues, and you want to use the dashboard to distinguish between failures in your own service and those caused by a Google Cloud service that you use. What should you do?

A. Enable Personalized Service Health annotations on the dashboard.
B. Create a log-based metric to track cloud service errors, and display the metric on the dashboard.
C. Create an alerting policy for the system error metrics.
D. Create a logs widget to display system errors from Cloud Logging on the dashboard.

Answer: A

NEW QUESTION # 158
You use Spinnaker to deploy your application and have created a canary deployment stage in the pipeline. Your application has an in-memory cache that loads objects at start time. You want to automate the comparison of the canary version against the production version. How should you configure the canary analysis?

A. Compare the canary with a new deployment of the previous production version.
B. Compare the canary with the existing deployment of the current production version.
C. Compare the canary with the average performance of a sliding window of previous production versions.
D. Compare the canary with a new deployment of the current production version.

Answer: C

NEW QUESTION # 159
You are ready to deploy a new feature of a web-based application to production. You want to use Google Kubernetes Engine (GKE) to perform a phased rollout to half of the web server pods.
What should you do?

A. Use a replica set in the deployment specification.
B. Use Node taints with NoExecute.
C. Use a partitioned rolling update.
D. Use a stateful set with parallel pod management policy.

Answer: C

Explanation:
https://medium.com/velotio-perspectives/exploring-upgrade-strategies-for-stateful-sets-in-kubernetes-c02b8286f251

NEW QUESTION # 160
Your organization recently adopted a container-based workflow for application development. Your team develops numerous applications that are deployed continuously through an automated build pipeline to a Kubernetes cluster in the production environment. The security auditor is concerned that developers or operators could circumvent automated testing and push code changes to production without approval. What should you do to enforce approvals?

A. Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.
B. Configure the build system with protected branches that require pull request approval.
C. Use an Admission Controller to verify that incoming requests originate from approved sources.
D. Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.

Answer: A

Explanation:
Explanation
The keywords here is "developers or operators". Option A the operators could push images to production without approval (operators could touch the cluster directly and the cluster cannot do any action against them).
Rest same as francisco_guerra.

NEW QUESTION # 161
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