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To prepare for the exam, candidates are advised to take relevant training courses, read the official study guide, and practice using the Google Cloud Platform. They should also have hands-on experience working with DevOps tools and technologies, such as Docker, Kubernetes, Jenkins, and Terraform. With the right preparation, candidates can pass the Google Professional-Cloud-DevOps-Engineer exam and join the elite group of Cloud DevOps experts who are in high demand in the IT industry.

Google Professional-Cloud-DevOps-Engineer Certification Exam is a highly coveted credential for professionals who want to demonstrate their expertise in cloud DevOps engineering. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam is designed to test the candidate's knowledge and skills in various cloud-based DevOps tools and technologies, including infrastructure automation, continuous integration and delivery, monitoring and logging, and security and compliance.

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q46-Q51):

NEW QUESTION # 46
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 multiple smaller build steps to minimize execution time.
  • B. Use Cloud Storage to cache intermediate artifacts.
  • C. Use larger Cloud Build virtual machines (VMs) by using the machine-type option.
  • D. Run multiple Jenkins agents to parallelize the build.

Answer: A


NEW QUESTION # 47
You are leading a DevOps project for your organization. The DevOps team is responsible for managing the service infrastructure and being on-call for incidents. The Software Development team is responsible for writing, submitting, and reviewing code. Neither team has any published SLOs. You want to design a new joint-ownership model for a service between the DevOps team and the Software Development team. Which responsibilities should be assigned to each team in the new joint-ownership model?

  • A. DevOps team responsibilitiesShared responsibilities for code reviewsSoftware Development team responsibilitiesManage the service infrastructureBe on-call for incidents on a rotation basisAdopt and publish SLOs for the serviceSubmit code to be reviewed
  • B. DevOps team responsibilitiesManage the service infrastructureBe on-call for incidentsSoftware Development team responsibilitiesAdopt and publish SLOs for the serviceSubmit code to be reviewedShared responsibilities for code reviews
  • C. DevOps team responsibilitiesManage the service infrastructureBe on-call for incidentsPerform code reviewsSoftware Development team responsibilitiesSubmit code to be reviewed by the DevOps teamPublish the SLOs that the DevOps team must meet
  • D. DevOps team responsibilitiesManage the service infrastructurePerform code reviewsSoftware Development team responsibilitiesSubmit code to be reviewed by the DevOps teamBe on-call for incidentsPublish the SLOs that the DevOps team must meet

Answer: B

Explanation:
Comprehensive and Detailed Explanation:
The best approach to a joint-ownership DevOps model follows SRE and DevOps best practices, where:
The DevOps team manages infrastructure and is on-call for incidents.
The Software Development team owns SLOs and submits code.
Code reviews should be a shared responsibility to ensure both teams maintain quality.
#Why not other options?
A (DevOps team reviewing all code)## Breaks DevOps best practices by making developers dependent on the DevOps team for code review.
B (DevOps does code reviews, developers handle incidents)## Developers should not handle on-call duties unless part of a shared rotation.
C (Developers manage infrastructure)## Developers should not be fully responsible for infrastructure.
#Official Reference:
SRE & DevOps Ownership Model
Shared Responsibility Model in Google Cloud


NEW QUESTION # 48
You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects Log entries for development projects must be exported to dev_dataset. and log entries for production projects must be exported to prod_dataset You need to minimize the number of log sinks created and you want to ensure that the log sinks apply to future projects What should you do?

  • A. Create a log sink in each project
  • B. Create an aggregated Iog sink in the Dev and Prod folders
  • C. Create two aggregated log sinks at the organization level, and filter by project ID
  • D. Create a single aggregated log sink at the organization level.

Answer: B

Explanation:
The best option for minimizing the number of log sinks created and ensuring that the log sinks apply to future projects is to create an aggregated log sink in the Dev and Prod folders. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. By creating an aggregated log sink in each folder, you can export log entries for development projects to dev_dataset and log entries for production projects to prod_dataset. You can also use filters to specify which logs you want to export. Additionally, by creating an aggregated log sink at the folder level, you can ensure that the log sink applies to future projects that are created under that folder.


NEW QUESTION # 49
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 BigQuery.
  • B. Enable managed collection on every GKE cluster. Query the metrics in Cloud Monitoring.
  • C. 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.
  • D. 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.

Answer: B

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 # 50
The new version of your containerized application has been tested and is ready to be deployed to production on Google Kubernetes Engine (GKE) You could not fully load-test the new version in your pre-production environment and you need to ensure that the application does not have performance problems after deployment Your deployment must be automated What should you do?

  • A. Deploy the application by using kubectl and set the spec. updatestrategy. type field to RollingUpdate Use Cloud Monitoring to look for performance issues, and run the kubectl rollback command if there are any issues.
  • B. Deploy the application through a continuous delivery pipeline by using blue/green deployments Migrate traffic to the new version of the application and use Cloud Monitoring to look for performance issues
  • C. Deploy the application through a continuous delivery pipeline by using canary deployments Use Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics
  • D. Deploy the application by using kubectl and use Config Connector to slowly ramp up traffic between versions. Use Cloud Monitoring to look for performance issues

Answer: C


NEW QUESTION # 51
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