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

SectionObjectives
Implement security and compliance- Secure CI/CD pipelines
  • 1. IAM and least privilege access
    • 2. Secret and credential management
      - Compliance and governance
      • 1. Audit logging and policy enforcement
        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
                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
                      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

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                          Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q90-Q95):

                          NEW QUESTION # 90
                          You are part of an organization that follows SRE practices and principles. You are taking over the management of a new service from the Development Team, and you conduct a Production Readiness Review (PRR). After the PRR analysis phase, you determine that the service cannot currently meet its Service Level Objectives (SLOs). You want to ensure that the service can meet its SLOs in production. What should you do next?

                          Answer: A


                          NEW QUESTION # 91
                          You are designing a system with three different environments: development, quality assurance (QA), and production.
                          Each environment will be deployed with Terraform and has a Google Kubemetes Engine (GKE) cluster created so that application teams can deploy their applications. Anthos Config Management will be used and templated to deploy infrastructure level resources in each GKE cluster. All users (for example, infrastructure operators and application owners) will use GitOps. How should you structure your source control repositories for both Infrastructure as Code (laC) and application code?

                          Answer: B

                          Explanation:
                          The correct answer is B. Cloud Infrastructure (Terraform) repository is shared: different directories are different environments. GKE Infrastructure (Anthos Config Management Kustomize manifests) repositories are separated: different branches are different environments. Application (app source code) repositories are separated: different branches are different features.
                          This answer follows the best practices for using Terraform and Anthos Config Management with GitOps, as described in the following sources:
                          For Terraform, it is recommended to use a single repository for all environments, and use directories to separate them. This way, you can reuse the same Terraform modules and configurations across environments, and avoid code duplication and drift.You can also use Terraform workspaces to isolate the state files for each environment12.
                          For Anthos Config Management, it is recommended to use separate repositories for each environment, and use branches to separate the clusters within each environment. This way, you can enforce different policies and configurations for each environment, and use pull requests to promote changes across environments.You can also use Kustomize to create overlays for each cluster that apply specific patches or customizations34.
                          For application code, it is recommended to use separate repositories for each application, and use branches to separate the features or bug fixes for each application. This way, you can isolate the development and testing of each application, and use pull requests to merge changes into the main branch.You can also use tags or labels to trigger deployments to different environments5.
                          References:
                          1:Best practices for using Terraform | Google Cloud
                          2: Terraform Recommended Practices - Part 1 | Terraform - HashiCorp Learn
                          3:Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog
                          4: Using Kustomize with Anthos Config Management | Anthos Config Management Documentation | Google Cloud
                          5: Deploy Anthos on GKE with Terraform part 3: Continuous Delivery with Cloud Build | Google Cloud Blog
                          6: GitOps-style continuous delivery with Cloud Build | Cloud Build Documentation | Google Cloud


                          NEW QUESTION # 92
                          You need to create a Cloud Monitoring SLO for a service that will be published soon. You want to verify that requests to the service will be addressed in fewer than 300 ms at least 90% Of the time per calendar month.
                          You need to identify the metric and evaluation method to use. What should you do?

                          Answer: D

                          Explanation:
                          The correct answer is A. Select a latency metric for a request-based method of evaluation.
                          A latency metric measures how responsive your service is to users.For example, you can use thecloud.
                          googleapis.com/http/server/response_latenciesmetric to measure the latency of HTTP requests to your service1. A request-based method of evaluation counts the number of successful requests that meet a certain criterion, such as being below a latency threshold, and compares it to the number of all requests.For example, you can define an SLI as the ratio of requests with latency below 300 ms to all requests2. A request-based method of evaluation is suitable for measuring performance over time, such as per calendar month.You can set an SLO for the SLI to be at least 90%, which means that you expect 90% of the requests to have latency below 300 ms in a month3.


                          NEW QUESTION # 93
                          You are reviewing your deployment pipeline in Google Cloud Deploy You must reduce toil in the pipeline and you want to minimize the amount of time it takes to complete an end-to-end deployment What should you do?
                          Choose 2 answers

                          Answer: C,D

                          Explanation:
                          The best options for reducing toil in the pipeline and minimizing the amount of time it takes to complete an end-to-end deployment are to create a trigger to notify the required team to complete the next step when manual intervention is required and to automate promotion approvals from the development environment to the test environment. A trigger is a resource that initiates a deployment when an event occurs, such as a code change, a schedule, or a manual request. You can create a trigger to notify the required team to complete the next step when manual intervention is required by using Cloud Build or Cloud Functions. This way, you can reduce the waiting time and human errors in the pipeline. A promotion approval is a process that allows you to approve or reject a deployment from one environment to another, such as from development to test. You can automate promotion approvals from the development environment to the test environment by using Google Cloud Deploy or Cloud Build. This way, you can speed up the deployment process and avoid manual steps.


                          NEW QUESTION # 94
                          You are ready to deploy a new feature of a web-based application to production. You want to use Google Kubernetes Engine (GKE) to perform a phased rollout to half of the web server pods.
                          What should you do?

                          Answer: A

                          Explanation:
                          https://medium.com/velotio-perspectives/exploring-upgrade-strategies-for-stateful-sets-in-kubernetes-c02b8286f251


                          NEW QUESTION # 95
                          ......

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