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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. Deployment automation strategies
    • 2. Release management practices
      - 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- System performance optimization
          • 1. Scaling strategies and load handling
            - Monitoring and observability
            • 1. Cloud Monitoring and Logging
              • 2. Performance tuning and feedback loops
                Topic 3: 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
                    Topic 4: 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

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                          Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q146-Q151):

                          NEW QUESTION # 146
                          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

                          Answer: D,E

                          Explanation:
                          Explanation
                          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.


                          NEW QUESTION # 147
                          Your team has recently deployed an NGINX-based application into Google Kubernetes Engine (GKE) and has exposed it to the public via an HTTP Google Cloud Load Balancer (GCLB) ingress. You want to scale the deployment of the application's frontend using an appropriate Service Level Indicator (SLI). What should you do?

                          Answer: B

                          Explanation:
                          Explanation
                          https://cloud.google.com/kubernetes-engine/docs/tutorials/autoscaling-metrics The Google Cloud HTTP Load Balancer (GCLB) provides metrics on the number of requests and the response latency for each backend service. These metrics can be used as custom metrics for the horizontal pod autoscaler (HPA) to scale the deployment based on the load. This is the correct solution to use an appropriate SLI for scaling.


                          NEW QUESTION # 148
                          Some of your production services are running in Google Kubernetes Engine (GKE) in the eu-west-1 region. Your build system runs in the us-west-1 region. You want to push the container images from your build system to a scalable registry to maximize the bandwidth for transferring the images to the cluster. What should you do?

                          Answer: B


                          NEW QUESTION # 149
                          You are configuring connectivity across Google Kubernetes Engine (GKE) clusters in different VPCs You notice that the nodes in Cluster A are unable to access the nodes in Cluster B You suspect that the workload access issue is due to the network configuration You need to troubleshoot the issue but do not have execute access to workloads and nodes You want to identify the layer at which the network connectivity is broken What should you do?

                          Answer: B

                          Explanation:
                          Explanation
                          The best option for troubleshooting the issue without having execute access to workloads and nodes is to use Network Connectivity Center to perform a Connectivity Test from Cluster A to Cluster B. Network Connectivity Center is a service that allows you to create, manage, and monitor network connectivity across Google Cloud, hybrid, and multi-cloud environments. You can use Network Connectivity Center to perform a Connectivity Test, which is a feature that allows you to test the reachability and latency between two endpoints, such as GKE clusters, VM instances, or IP addresses. By using Network Connectivity Center to perform a Connectivity Test from Cluster A to Cluster B, you can identify the layer at which the network connectivity is broken, such as the firewall, routing, or load balancing.


                          NEW QUESTION # 150
                          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: A

                          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.


                          NEW QUESTION # 151
                          ......

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