100% PASS PROFESSIONAL-CLOUD-DEVOPS-ENGINEER RELIABLE TEST LABS - GOOGLE CLOUD CERTIFIED - PROFESSIONAL CLOUD DEVOPS ENGINEER EXAM REALISTIC VALID TORRENT

100% Pass Professional-Cloud-DevOps-Engineer Reliable Test Labs - Google Cloud Certified - Professional Cloud DevOps Engineer Exam Realistic Valid Torrent

100% Pass Professional-Cloud-DevOps-Engineer Reliable Test Labs - Google Cloud Certified - Professional Cloud DevOps Engineer Exam Realistic Valid Torrent

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To become a Google Cloud Certified Professional Cloud DevOps Engineer, candidates must pass the certification exam, which consists of multiple-choice and multiple-select questions. Professional-Cloud-DevOps-Engineer Exam covers a wide range of topics, including GCP services such as Kubernetes Engine, Cloud Functions, and Cloud Run, as well as DevOps practices such as continuous integration and delivery, monitoring and logging, and infrastructure as code. Candidates must also demonstrate their ability to troubleshoot issues and optimize performance in a GCP environment. Overall, the certification exam is a rigorous test of a candidate's skills and knowledge in deploying and managing applications on GCP, and passing it is a significant achievement for any DevOps professional.

To prepare for the exam, candidates can take advantage of various resources provided by Google Cloud, such as online courses, practice exams, and study guides. Additionally, candidates can gain hands-on experience by working on Google Cloud projects and participating in online forums and communities.

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
You have a CI/CD pipeline that uses Cloud Build to build new Docker images and push them to Docker Hub. You use Git for code versioning. After making a change in the Cloud Build YAML configuration, you notice that no new artifacts are being built by the pipeline. You need to resolve the issue following Site Reliability Engineering practices. What should you do?

  • A. Run a Git compare between the previous and current Cloud Build Configuration files to find and fix the bug.
  • B. Upload the configuration YAML file to Cloud Storage and use Error Reporting to identify and fix the issue.
  • C. Change the CI pipeline to push the artifacts to Container Registry instead of Docker Hub.
  • D. Disable the CI pipeline and revert to manually building and pushing the artifacts.

Answer: C


NEW QUESTION # 57
You support a popular mobile game application deployed on Google Kubernetes Engine (GKE) across several Google Cloud regions. Each region has multiple Kubernetes clusters. You receive a report that none of the users in a specific region can connect to the application. You want to resolve the incident while following Site Reliability Engineering practices. What should you do first?

  • A. Use Stackdriver Logging to filter on the clusters in the affected region, and inspect error messages in the logs.
  • B. Add an extra node pool that consists of high memory and high CPU machine type instances to the cluster.
  • C. Reroute the user traffic from the affected region to other regions that don't report issues.
  • D. Use Stackdriver Monitoring to check for a spike in CPU or memory usage for the affected region.

Answer: C

Explanation:
Google always aims to first stop the impact of an incident, and then find the root cause (unless the root cause just happens to be identified early on).


NEW QUESTION # 58
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 canary deployment, and test your new application periodically after the new version is deployed
  • C. Perform a rolling deployment and test your new application after the deployment is complete
  • D. Perform A. B testing, and test your application periodically after the deployment is complete

Answer: A


NEW QUESTION # 59
You have a CI/CD pipeline that uses Cloud Build to build new Docker images and push them to Docker Hub.
You use Git for code versioning. After making a change in the Cloud Build YAML configuration, you notice that no new artifacts are being built by the pipeline. You need to resolve the issue following Site Reliability Engineering practices. What should you do?

  • A. Run a Git compare between the previous and current Cloud Build Configuration files to find and fix the bug.
  • B. Upload the configuration YAML file to Cloud Storage and use Error Reporting to identify and fix the issue.
  • C. Disable the CI pipeline and revert to manually building and pushing the artifacts.
  • D. Change the CI pipeline to push the artifacts is Container Registry instead of Docker Hub.

Answer: D


NEW QUESTION # 60
Your company runs services by using multiple globally distributed Google Kubernetes Engine (GKE) clusters Your operations team has set up workload monitoring that uses Prometheus-based tooling for metrics alerts:
and generating dashboards This setup does not provide a method to view metrics globally across all clusters You need to implement a scalable solution to support global Prometheus querying and minimize management overhead What should you do?

  • A. Configure Prometheus cross-service federation for centralized data access
  • B. Configure workload metrics within Cloud Operations for GKE
  • C. Configure Prometheus hierarchical federation for centralized data access
  • D. Configure Google Cloud Managed Service for Prometheus

Answer: D

Explanation:
The best option for implementing a scalable solution to support global Prometheus querying and minimize management overhead is to use Google Cloud Managed Service for Prometheus. Google Cloud Managed Service for Prometheus is a fully managed service that allows you to collect, query, and visualize metrics from your GKE clusters using Prometheus-based tooling. You can use Google Cloud Managed Service for Prometheus to query metrics across multiple clusters and regions using a global view. You can also use Google Cloud Managed Service for Prometheus to integrate with other Google Cloud services, such as Cloud Monitoring, Cloud Logging, and BigQuery. By using Google Cloud ManagedService for Prometheus, you can avoid managing and scaling your own Prometheus servers and focus on your application performance.


NEW QUESTION # 61
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