Actual Google Professional-Cloud-DevOps-Engineer Exam Questions and Answers

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Google Professional-Cloud-DevOps-Engineer Exam Syllabus Topics:

Section	Objectives
Topic 1: Implement site reliability engineering (SRE) practices	- Define and manage SLI, SLO, and SLA 1. Error budgets and monitoring strategies - Incident management and postmortems 1. Alerting and on-call practices
Topic 2: Optimize performance and continuous delivery	- System performance optimization 1. Scaling strategies and load handling - Monitoring and observability 1. Cloud Monitoring and Logging 2. Performance tuning and feedback loops
Topic 3: Develop and implement CI/CD pipelines	- Build and manage CI/CD pipelines using Google Cloud tools 1. Cloud Build pipeline design 2. Artifact repository management - Automate build, test, and deployment processes 1. Release management practices 2. Deployment automation strategies
Topic 4: Implement security and compliance	- Secure CI/CD pipelines 1. IAM and least privilege access 2. Secret and credential management - Compliance and governance 1. Audit logging and policy enforcement

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q125-Q130):

NEW QUESTION # 125
You use Google Cloud Managed Service for Prometheus with managed collection to gather metrics from your service running on Google Kubernetes Engine (GKE). After deploying the service, there is no metric data appearing in Cloud Monitoring, and you have not encountered any error messages. You need to troubleshoot this issue. What should you do?

A. Verify that your PodMonitoring configuration references a valid port.
B. Determine if your service has exceeded its quota for writes to the Cloud Monitoring API.
C. Confirm that your service has the monitoring.servicesViewer IAM role.
D. Check if the Grafana service is installed on your GKE cluster.

Answer: A

NEW QUESTION # 126
You are managing the production deployment to a set of Google Kubernetes Engine (GKE) clusters. You want to make sure only images which are successfully built by your trusted CI/CD pipeline are deployed to production. What should you do?

A. Enable Vulnerability Analysis on the Container Registry.
B. Enable Cloud Security Scanner on the clusters.
C. Set up the Kubernetes Engine clusters as private clusters.
D. Set up the Kubernetes Engine clusters with Binary Authorization.

Answer: D

Explanation:
Explanation
https://cloud.google.com/binary-authorization/docs/overview

NEW QUESTION # 127
You use Cloud Build to build and deploy your application. You want to securely incorporate database credentials and other application secrets into the build pipeline. You also want to minimize the development effort. What should you do?

A. Encrypt the secrets and store them in the application repository. Store a decryption key in a separate repository and grant Cloud Build access to the repository.
B. Create a Cloud Storage bucket and use the built-in encryption at rest. Store the secrets in the bucket and grant Cloud Build access to the bucket.
C. Use Cloud Key Management Service (Cloud KMS) to encrypt the secrets and include them in your Cloud Build deployment configuration. Grant Cloud Build access to the KeyRing.
D. Use client-side encryption to encrypt the secrets and store them in a Cloud Storage bucket. Store a decryption key in the bucket and grant Cloud Build access to the bucket.

Answer: C

Explanation:
Explanation
https://cloud.google.com/build/docs/securing-builds/use-encrypted-credentials

NEW QUESTION # 128
You are performing a semiannual capacity planning exercise for your flagship service. You expect a service user growth rate of 10% month-over-month over the next six months. Your service is fully containerized and runs on Google Cloud Platform (GCP). using a Google Kubernetes Engine (GKE) Standard regional cluster on three zones with cluster autoscaler enabled. You currently consume about 30% of your total deployed CPU capacity, and you require resilience against the failure of a zone. You want to ensure that your users experience minimal negative impact as a result of this growth or as a result of zone failure, while avoiding unnecessary costs. How should you prepare to handle the predicted growth?

A. Verity the maximum node pool size, enable a horizontal pod autoscaler, and then perform a load test to verity your expected resource needs.
B. Because you are at only 30% utilization, you have significant headroom and you won't need to add any additional capacity for this rate of growth.
C. Because you are deployed on GKE and are using a cluster autoscaler. your GKE cluster will scale automatically, regardless of growth rate.
D. Proactively add 60% more node capacity to account for six months of 10% growth rate, and then perform a load test to make sure you have enough capacity.

Answer: C

NEW QUESTION # 129
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. B testing, and test your application periodically after the deployment is complete
B. Perform a rolling deployment and test your new application after the deployment is complete
C. Perform a blue/green deployment and test your new application after the deployment is complete
D. Perform a canary deployment, and test your new application periodically after the new version is deployed

Answer: C

Explanation:
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 # 130
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