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

SectionObjectives
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- Monitoring and observability
        • 1. Performance tuning and feedback loops
          • 2. Cloud Monitoring and Logging
            - 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
                  Develop and implement CI/CD pipelines- Build and manage CI/CD pipelines using Google Cloud tools
                  • 1. Cloud Build pipeline design
                    • 2. Artifact repository management
                      - Automate build, test, and deployment processes
                      • 1. Release management practices
                        • 2. Deployment automation strategies

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                          Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q12-Q17):

                          NEW QUESTION # 12
                          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?

                          Answer: D


                          NEW QUESTION # 13
                          You are using Terraform to manage infrastructure as code within a Cl/CD pipeline You notice that multiple copies of the entire infrastructure stack exist in your Google Cloud project, and a new copy is created each time a change to the existing infrastructure is made You need to optimize your cloud spend by ensuring that only a single instance of your infrastructure stack exists at a time. You want to follow Google-recommended practices What should you do?

                          Answer: A

                          Explanation:
                          Explanation
                          The best option for optimizing your cloud spend by ensuring that only a single instance of your infrastructure stack exists at a time is to confirm that the pipeline is storing and retrieving the terraform.tfstate file from Cloud Storage with the Terraform gcs backend. The terraform.tfstate file is a file that Terraform uses to store the current state of your infrastructure. The Terraform gcs backend is a backend type that allows you to store the terraform.tfstate file in a Cloud Storage bucket. By using the Terraform gcs backend, you can ensure that your pipeline has access to the latest state of your infrastructure and avoid creating multiple copies of the entire infrastructure stack.


                          NEW QUESTION # 14
                          You are configuring your CI/CD pipeline natively on Google Cloud. You want builds in a pre-production Google Kubernetes Engine (GKE) environment to be automatically load-tested before being promoted to the production GKE environment. You need to ensure that only builds that have passed this test are deployed to production. You want to follow Google-recommended practices. How should you configure this pipeline with Binary Authorization?

                          Answer: D

                          Explanation:
                          The correct answer is B. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) authenticated through Workload Identity.
                          According to the Google Cloud documentation, Binary Authorization is a deploy-time security control that ensures only trusted container images are deployed on Google Kubernetes Engine (GKE) or Cloud Run1.
                          Binary Authorization uses attestations to certify that a specific image has completed a previous stage in the CI
                          /CD pipeline, such as passing a load test2.Attestations are signed by private keys that are associated with attestors, which are entities that verify the attestations3.To follow Google-recommended practices, you should store your private keys in Cloud Key ManagementService (Cloud KMS), which is a secure and scalable service for managing cryptographic keys4.You should also use Workload Identity, which is a feature that allows Kubernetes service accounts to act as Google service accounts, to authenticate to Cloud KMS and sign attestations without having to manage or expose service account keys5.
                          The other options are incorrect because they do not follow Google-recommended practices. Option A and option D require human intervention to sign the attestations, which is not scalable or automated. Option C exposes the service account JSON key as a Kubernetes Secret, which is less secure than using Workload Identity.
                          Reference:
                          Creating an attestor, Creating an attestor.Cloud Key Management Service Documentation, Overview.
                          Attestations overview, Attestations overview.Using Workload Identity with Binary Authorization, Using Workload Identity with Binary Authorization.Binary Authorization, Binary Authorization.


                          NEW QUESTION # 15
                          Your team deploys applications to three Google Kubernetes Engine (GKE) environments development staging and production You use GitHub reposrtones as your source of truth You need to ensure that the three environments are consistent You want to follow Google-recommended practices to enforce and install network policies and a logging DaemonSet on all the GKE clusters in those environments What should you do?

                          Answer: C

                          Explanation:
                          Explanation
                          The best option for ensuring that the three environments are consistent and following Google-recommended practices is to use Cloud Build to render and deploy the network policies and the DaemonSet, and set up Config Sync to sync the configurations for the three environments. Cloud Build is a service that executes your builds on Google Cloud infrastructure. You can use Cloud Build to render and deploy your network policies and DaemonSet as code using tools like Kustomize, Helm, or kpt. Config Sync is a feature that enables you to manage the configurations of your GKE clusters from a single source of truth, such as a Git repository. You can use Config Sync to sync the configurations for your development, staging, and production environments and ensure that they are consistent.


                          NEW QUESTION # 16
                          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?

                          Answer: D

                          Explanation:
                          "After making a change in the Cloud Build YAML configuration, you notice that no new artifacts are being built by the pipeline"- means something wrong on the recent change not with the image registry.


                          NEW QUESTION # 17
                          ......

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