Professional-Cloud-DevOps-Engineer Exam Actual Questions - Professional-Cloud-DevOps-Engineer Exam Tests

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

SectionObjectives
Topic 1: Develop and implement CI/CD pipelines- Automate build, test, and deployment processes
  • 1. Release management practices
    • 2. Deployment automation strategies
      - Build and manage CI/CD pipelines using Google Cloud tools
      • 1. Artifact repository management
        • 2. Cloud Build pipeline design
          Topic 2: Optimize performance and continuous delivery- Monitoring and observability
          • 1. Cloud Monitoring and Logging
            • 2. Performance tuning and feedback loops
              - System performance optimization
              • 1. Scaling strategies and load handling
                Topic 3: Implement security and compliance- Compliance and governance
                • 1. Audit logging and policy enforcement
                  - Secure CI/CD pipelines
                  • 1. IAM and least privilege access
                    • 2. Secret and credential management
                      Topic 4: 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

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                          Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q202-Q207):

                          NEW QUESTION # 202
                          Your company has a Google Cloud resource hierarchy with folders for production test and development Your cyber security team needs to review your company's Google Cloud security posture to accelerate security issue identification and resolution You need to centralize the logs generated by Google Cloud services from all projects only inside your production folder to allow for alerting and near-real time analysis. What should you do?

                          Answer: B

                          Explanation:
                          Explanation
                          The best option for centralizing the logs generated by Google Cloud services from all projects only inside your production folder is to create an aggregated log sink associated with the production folder that uses a Cloud Logging bucket as the destination. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. A Cloud Logging bucket is a storage location for logs that can be used as a destination for log sinks. By creating an aggregated log sink with a Cloud Logging bucket, you can collect and store all the logs from the production folder in one place and allow for alerting and near-real time analysis using Cloud Monitoring and Cloud Operations.


                          NEW QUESTION # 203
                          As part of your company's initiative to shift left on security, the infoSec team is asking all teams to implement guard rails on all the Google Kubernetes Engine (GKE) clusters to only allow the deployment of trusted and approved images You need to determine how to satisfy the InfoSec teams goal of shifting left on security. What should you do?

                          Answer: A


                          NEW QUESTION # 204
                          Your company operates in a highly regulated domain that requires you to store all organization logs for seven years You want to minimize logging infrastructure complexity by using managed services You need to avoid any future loss of log capture or stored logs due to misconfiguration or human error What should you do?

                          Answer: D


                          NEW QUESTION # 205
                          You work for a global organization and are running a monolithic application on Compute Engine You need to select the machine type for the application to use that optimizes CPU utilization by using the fewest number of steps You want to use historical system metncs to identify the machine type for the application to use You want to follow Google-recommended practices What should you do?

                          Answer: D

                          Explanation:
                          Explanation
                          The best option for selecting the machine type for the application to use that optimizes CPU utilization by using the fewest number of steps is to use the Recommender API and apply the suggested recommendations.
                          The Recommender API is a service that provides recommendations for optimizing your Google Cloud resources, such as Compute Engine instances, disks, and firewalls. You can use the Recommender API to get recommendations for changing the machine type of your Compute Engine instances based on historical system metrics, such as CPU utilization. You can also apply the suggested recommendations by using the Recommender API or Cloud Console. This way, you can optimize CPU utilization by using the most suitable machine type for your application with minimal effort.


                          NEW QUESTION # 206
                          You have an application that runs in Google Kubernetes Engine (GKE). The application consists of several microservices that are deployed to GKE by using Deployments and Services One of the microservices is experiencing an issue where a Pod returns 403 errors after the Pod has been running for more than five hours Your development team is working on a solution but the issue will not be resolved for a month You need to ensure continued operations until the microservice is fixed You want to follow Google-recommended practices and use the fewest number of steps What should you do?

                          Answer: A

                          Explanation:
                          Explanation
                          The best option for ensuring continued operations until the microservice is fixed is to add a HTTP liveness probe to the microservice's deployment. A HTTP liveness probe is a type of probe that checks if a Pod is alive by sending an HTTP request and expecting a success response code. If the probe fails, Kubernetes will restart the Pod. You can add a HTTP liveness probe to your microservice's deployment by using a livenessProbe field in your Pod spec. This way, you can ensure that any Pod that returns 403 errors after running for more than five hours will be restarted automatically and resume normal operations.


                          NEW QUESTION # 207
                          ......

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