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The Professional-Cloud-DevOps-Engineer certification exam consists of multiple-choice and multiple-select questions, and candidates have two hours and thirty minutes to complete the exam. Professional-Cloud-DevOps-Engineer exam is computer-based and can be taken at any Google Cloud Partner testing center or online through the ProctorU platform. Professional-Cloud-DevOps-Engineer Exam costs $200, and candidates need to pass with a score of 70% or higher to earn their certification.

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Upon successful completion of the certification exam, candidates will receive a Google Cloud Certified - Professional Cloud DevOps Engineer certificate, which demonstrates their proficiency in cloud-based DevOps engineering. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is recognized globally and is highly valued by employers, making it an excellent investment for professionals seeking to advance their career in this field.

Google Professional-Cloud-DevOps-Engineer Certification Exam consists of multiple-choice questions and scenario-based questions. Professional-Cloud-DevOps-Engineer exam is administered online and can be taken from anywhere in the world. Candidates must achieve a score of 70% or higher to pass the exam and earn the certification.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q158-Q163):

NEW QUESTION # 158
Some of your production services are running in Google Kubernetes Engine (GKE) in the eu-west-1 region. Your build system runs in the us-west-1 region. You want to push the container images from your build system to a scalable registry to maximize the bandwidth for transferring the images to the cluster. What should you do?

A. Push the images to Google Container Registry (GCR) using the us.gcr.io hostname.
B. Push the images to a private image registry running on a Compute Engine instance in the eu-west-1 region.
C. Push the images to Google Container Registry (GCR) using the eu.gcr.io hostname.
D. Push the images to Google Container Registry (GCR) using the gcr.io hostname.

Answer: C

NEW QUESTION # 159
Your application artifacts are being built and deployed via a CI/CD pipeline. You want the CI/CD pipeline to securely access application secrets. You also want to more easily rotate secrets in case of a security breach.
What should you do?

A. Store secrets in a separate configuration file on Git. Provide select developers with access to the configuration file.
B. Encrypt the secrets and store them in the source code repository. Store a decryption key in a separate repository and grant your pipeline access to it.
C. Store secrets in Cloud Storage encrypted with a key from Cloud KMS. Provide the CI/CD pipeline with access to Cloud KMS via IAM.
D. Prompt developers for secrets at build time. Instruct developers to not store secrets at rest.

Answer: C

NEW QUESTION # 160
You are designing a continuous delivery (CD) strategy for a new serverless application. The application is packaged as a container image, stored in Artifact Registry, and deployed to Cloud Run. Your design requires a staging environment, a fully-managed Google Cloud service, mandatory manual approval for production deployments, and a phased rollout to production. Your solution should minimize administrative overhead.
What should you do?

A. Use Cloud Build to create a multi-stage pipeline. Configure the trigger to require approval before starting the build. Use the deploy command with the --traffic flag to incrementally shift traffic to the new revision in production.
B. Use a Cloud Build trigger to initiate a GitOps workflow. Configure the trigger to update a manifest in a Git repository, which a controller on a GKE Autopilot cluster then synchronizes to manage a phased traffic rollout to the new revision.
C. Use Cloud Deploy to define a single delivery pipeline that promotes a release between a staging target and a production target. Configure the production target to require approval and to automatically execute a phased rollout that incrementally shifts traffic.
D. Define two separate Cloud Deploy pipelines. Configure the first pipeline to deploy to staging, and configure the second pipeline to trigger and execute a phased, canary rollout to the production Cloud Run service.

Answer: C

Explanation:
Comprehensive and Detailed 150 to 200 words of Explanation From Google Cloud DevOps guides documents:
Google Cloud Deploy is the platform's premier, fully-managed continuous delivery service specifically designed to simplify the progression of software through a series of environments (targets). For a Cloud Run application, Cloud Deploy provides a native, low-overhead way to define a single delivery pipeline that encapsulates both the staging and production targets. This approach directly satisfies the requirement for a fully-managed service while minimizing administrative toil compared to custom-scripted Cloud Build workflows (Option C) or complex GitOps controllers on GKE (Option B).
Key features of Cloud Deploy relevant here are its built-in support for mandatory manual approvals and automated canary/phased rollouts. By defining these in the delivery-pipeline YAML, you can ensure that a release is only promoted to production after a designated user approves it in the console or via CLI. Once approved, Cloud Deploy manages the traffic shifting to the new Cloud Run revision incrementally according to your specified percentages. This eliminates the need for managing multiple pipelines (Option D) and leverages native platform capabilities to ensure a reliable, repeatable, and secure deployment process that adheres to Google Cloud's modern DevOps and SRE deployment standards.

NEW QUESTION # 161
Your team uses Cloud Build for all CI/CO pipelines. You want to use the kubectl builder for Cloud Build to deploy new images to Google Kubernetes Engine (GKE). You need to authenticate to GKE while minimizing development effort. What should you do?

A. Specify the Container Developer role for Cloud Build in the cloudbuild.yaml file.
B. Create a new service account with the Container Developer role and use it to run Cloud Build.
C. Assign the Container Developer role to the Cloud Build service account.
D. Create a separate step in Cloud Build to retrieve service account credentials and pass these to kubectl.

Answer: C

NEW QUESTION # 162
Your organization is running multiple Google Kubernetes Engine (GKE) clusters in a project. You need to design a highly-available solution to collect and query both domain-specific workload metrics and GKE default metrics across all clusters, while minimizing operational overhead. What should you do?

A. Enable managed collection on every GKE cluster. Query the metrics in Cloud Monitoring.
B. Use Prometheus Operator to install Prometheus in every cluster and scrape the metrics. Configure remote-write to one central Prometheus. Query the central Prometheus instance.
C. Enable managed collection on every GKE cluster. Query the metrics in BigQuery.
D. Use Prometheus Operator to install Prometheus in every cluster and scrape the metrics. Ensure that a Thanos sidecar is enabled on every Prometheus instance. Configure Thanos in the central cluster. Query the central Thanos instance.

Answer: A

Explanation:
Google recommends using managed collection for GKE monitoring to reduce operational overhead. This sends Kubernetes metrics and custom workload metrics directly to Cloud Monitoring.
"You can enable Managed Service for Prometheus, which provides a fully managed Prometheus-compatible monitoring solution integrated with Cloud Monitoring."
- Managed Collection in GKE
"It automatically collects and stores metrics in Cloud Monitoring with no need to manage a Prometheus server."
- Google Cloud Managed Prometheus
It's scalable, cost-effective, and requires no additional infrastructure like Thanos or Prometheus federation.

NEW QUESTION # 163
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