PROFESSIONAL-CLOUD-DEVOPS-ENGINEER VALID TEST QUESTIONS & EXAMCOLLECTION PROFESSIONAL-CLOUD-DEVOPS-ENGINEER QUESTIONS ANSWERS

Professional-Cloud-DevOps-Engineer Valid Test Questions & Examcollection Professional-Cloud-DevOps-Engineer Questions Answers

Professional-Cloud-DevOps-Engineer Valid Test Questions & Examcollection Professional-Cloud-DevOps-Engineer Questions Answers

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The desktop Google Cloud Certified - Professional Cloud DevOps Engineer Exam (Professional-Cloud-DevOps-Engineer) practice exam software helps its valued customer to be well aware of the pattern of the real Professional-Cloud-DevOps-Engineer exam. You can try a free Google Cloud Certified - Professional Cloud DevOps Engineer Exam (Professional-Cloud-DevOps-Engineer) demo too. This Google Cloud Certified - Professional Cloud DevOps Engineer Exam (Professional-Cloud-DevOps-Engineer) practice test is customizable and you can adjust its time and Google PDF Questions. Exam4Tests helps you in doing self-assessment so that you reduce your chances of failure in the examination of Google Cloud Certified - Professional Cloud DevOps Engineer Exam (Professional-Cloud-DevOps-Engineer) certification.

Google Cloud Certified - Professional Cloud DevOps Engineer is a certification exam that validates the skills of the candidates in the field of cloud computing and DevOps. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam is designed for professionals who have a good understanding of DevOps practices and tools, and can implement them in the Google Cloud Platform (GCP) environment. Professional-Cloud-DevOps-Engineer exam is intended to test the candidate's ability to design, develop, and manage a reliable, scalable, and secure cloud-based infrastructure.

Google Professional-Cloud-DevOps-Engineer Exam is a performance-based exam that evaluates the candidate's practical skills in the real-world scenario. Professional-Cloud-DevOps-Engineer exam is conducted online, and the candidates are required to complete a set of tasks within a given time frame. Professional-Cloud-DevOps-Engineer exam is divided into two parts - a scenario-based section and a hands-on section. The scenario-based section tests the candidate's ability to analyze and solve complex problems, while the hands-on section evaluates the candidate's skills in implementing and managing GCP solutions.

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q164-Q169):

NEW QUESTION # 164
You use Cloud Build to build your application. You want to reduce the build time while minimizing cost and development effort. What should you do?

  • A. Use larger Cloud Build virtual machines (VMs) by using the machine-type option.
  • B. Use Cloud Storage to cache intermediate artifacts.
  • C. Use multiple smaller build steps to minimize execution time.
  • D. Run multiple Jenkins agents to parallelize the build.

Answer: C


NEW QUESTION # 165
You use Terraform to manage an application deployed to a Google Cloud environment The application runs on instances deployed by a managed instance group The Terraform code is deployed by using a CI/CD pipeline When you change the machine type on the instance template used by the managed instance group, the pipeline fails at the terraform apply stage with the following error message

You need to update the instance template and minimize disruption to the application and the number of pipeline runs What should you do?

  • A. Set the create_bef ore_destroy meta-argument to true in the lifecycle block on the instance template
  • B. Add a new instance template update the managed instance group to use the new instance template and delete the old instance template
  • C. Remove the managed instance group from the Terraform state file update the instance template and reimport the managed instance group.
  • D. Delete the managed instance group and recreate it after updating the instance template

Answer: A

Explanation:
The best option for updating the instance template and minimizing disruption to the application and the number of pipeline runs is to set the create_before_destroy meta-argument to true in the lifecycle block on the instance template. The create_before_destroy meta-argument is a Terraform feature that specifies that a new resource should be created before destroying an existing one during an update. This way, you can avoid downtime and errors when updating a resource that is in use by another resource, such as an instance template that is used by a managed instance group. By setting the create_before_destroy meta-argument to true in the lifecycle block on the instance template, you can ensure that Terraform creates a new instance template with the updated machine type, updates the managed instance group to use the new instance template, and then deletes the old instance template.


NEW QUESTION # 166
You are building and running client applications in Cloud Run and Cloud Functions Your client requires that all logs must be available for one year so that the client can import the logs into their logging service You must minimize required code changes What should you do?

  • A. Create a Pub/Sub topic subscription and logging sink Configure the logging sink to send all logs into the topic Give your client access to the topic to retrieve the logs
  • B. Update all images in Cloud Run and all functions in Cloud Functions to send logs to both Cloud Logging and the client's logging service Ensure that all the ports required to send logs are open in the VPC firewall
  • C. Create a storage bucket and appropriate VPC firewall rules Update all images in Cloud Run and all functions in Cloud Functions to send logs to a file within the storage bucket
  • D. Create a logs bucket and logging sink. Set the retention on the logs bucket to 365 days Configure the logging sink to send logs to the bucket Give your client access to the bucket to retrieve the logs

Answer: D

Explanation:
Explanation
The best option for storing all logs for one year and minimizing required code changes is to create a logs bucket and logging sink, set the retention on the logs bucket to 365 days, configure the logging sink to send logs to the bucket, and give your client access to the bucket to retrieve the logs. A logs bucket is a Cloud Storage bucket that is used to store logs from Cloud Logging. A logging sink is a resource that defines where log entries are sent, such as a logs bucket, BigQuery dataset, or Pub/Sub topic. You can create a logs bucket and logging sink in Cloud Logging and set the retention on the logs bucket to 365 days. This way, you can ensure that all logs are stored for one year and protected from deletion. You can also configure the logging sink to send logs from Cloud Run and Cloud Functions to the logs bucket without any code changes. You can then give your client access to the logs bucket by using IAM policies or signed URLs.


NEW QUESTION # 167
Your team is designing a new application for deployment both inside and outside Google Cloud Platform (GCP). You need to collect detailed metrics such as system resource utilization. You want to use centralized GCP services while minimizing the amount of work required to set up this collection system. What should you do?

  • A. Import the Stackdriver Debugger package, and configure the application to emit debug messages with timing information.
  • B. Instrument the code using a timing library, and publish the metrics via a health check endpoint that is scraped by Stackdriver.
  • C. Import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis.
  • D. Install an Application Performance Monitoring (APM) tool in both locations, and configure an export to a central data storage location for analysis.

Answer: A


NEW QUESTION # 168
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. Upload the configuration YAML file to Cloud Storage and use Error Reporting to identify and fix the issue.
  • B. Change the CI pipeline to push the artifacts is Container Registry instead of Docker Hub.
  • C. Run a Git compare between the previous and current Cloud Build Configuration files to find and fix the bug.
  • D. Disable the CI pipeline and revert to manually building and pushing the artifacts.

Answer: B


NEW QUESTION # 169
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