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

SectionObjectives
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
          Implement site reliability engineering (SRE) practices- Incident management and postmortems
          • 1. Alerting and on-call practices
            - Define and manage SLI, SLO, and SLA
            • 1. Error budgets and monitoring strategies
              Implement security and compliance- Secure CI/CD pipelines
              • 1. Secret and credential management
                • 2. IAM and least privilege access
                  - Compliance and governance
                  • 1. Audit logging and policy enforcement
                    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

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                          Google Cloud Certified - Professional Cloud DevOps Engineer Exam Professional-Cloud-DevOps-Engineer Prüfungsfragen mit Lösungen (Q99-Q104):

                          99. Frage
                          You are performing a semi-annual capacity planning exercise for your flagship service You expect a service user growth rate of 10% month-over-month for the next six months Your service is fully containerized and runs on a Google Kubemetes Engine (GKE) standard cluster across three zones with cluster autoscaling 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 o' as a result of zone failure while you avoid unnecessary costs How should you prepare to handle the predicted growth?

                          Antwort: A

                          Begründung:
                          Explanation
                          The best option for preparing to handle the predicted growth is to verify the maximum node pool size, enable a Horizontal Pod Autoscaler, and then perform a load test to verify your expected resource needs. The maximum node pool size is a parameter that specifies the maximum number of nodes that can be added to a node pool by the cluster autoscaler. You should verify that the maximum node pool size is sufficient to accommodate your expected growth rate and avoid hitting any quota limits. The Horizontal Pod Autoscaler is a feature that automatically adjusts the number of Pods in a deployment or replica set based on observed CPU utilization or custom metrics. You should enable a Horizontal Pod Autoscaler for your application to ensure that it runs enough Pods to handle the load. A load test is a test that simulates high user traffic and measures the performance and reliability of your application. You should perform a load test to verify your expected resource needs and identify any bottlenecks or issues.


                          100. Frage
                          You are running a web application deployed to a Compute Engine managed instance group Ops Agent is installed on all instances You recently noticed suspicious activity from a specific IP address You need to configure Cloud Monitoring to view the number of requests from that specific IP address with minimal operational overhead. What should you do?

                          Antwort: C


                          101. Frage
                          Your organization uses a change advisory board (CAB) to approve all changes to an existing service You want to revise this process to eliminate any negative impact on the software delivery performance What should you do?
                          Choose 2 answers

                          Antwort: C,E

                          Begründung:
                          A change advisory board (CAB) is a traditional way of approving changes to a service, but it can slow down the software delivery performance and introduce bottlenecks. A better way to improve the speed and quality of changes is to use a peer-review based process for individual changes that is enforced at code check-in time and supported by automated tests. This way, developers can get fast feedback on the impact of their changes and catch any errors or bugs before they reach production. Additionally, the team's development platform should enable developers to get fast feedback on the impact of their changes, such as using Cloud Code, Cloud Build, or Cloud Debugger.


                          102. Frage
                          You are managing an application that runs in Compute Engine The application uses a custom HTTP server to expose an API that is accessed by other applications through an internal TCP/UDP load balancer A firewall rule allows access to the API port from 0.0.0-0/0. You need to configure Cloud Logging to log each IP address that accesses the API by using the fewest number of steps What should you do Bret?

                          Antwort: D


                          103. Frage
                          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?

                          Antwort: B

                          Begründung:
                          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 Managed Service for Prometheus, you can avoid managing and scaling your own Prometheus servers and focus on your application performance.


                          104. Frage
                          ......

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