Professional-Cloud-DevOps-Engineer - Google Cloud Certified - Professional Cloud DevOps Engineer Exam Latest New Exam Materials

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

SectionObjectives
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
          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
                    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

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                          Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q205-Q210):

                          NEW QUESTION # 205
                          You are leading a DevOps project for your organization. The DevOps team is responsible for managing the service infrastructure and being on-call for incidents. The Software Development team is responsible for writing, submitting, and reviewing code. Neither team has any published SLOs. You want to design a new joint-ownership model for a service between the DevOps team and the Software Development team. Which responsibilities should be assigned to each team in the new joint-ownership model?

                          Answer: A


                          NEW QUESTION # 206
                          Your company is migrating its production systems to Google Cloud. You need to implement site reliability engineering (SRE) practices during the migration to minimize customer impact from potential future incidents. Which two SRE practices should you implement?
                          Choose 2 answers

                          Answer: B,E


                          NEW QUESTION # 207
                          You are deploying an application to Cloud Run. The application requires a password to start. Your organization requires that all passwords are rotated every 24 hours, and your application must have the latest password. You need to deploy the application with no downtime. What should you do?

                          Answer: D

                          Explanation:
                          The correct answer is B, Store the password in Secret Manager and mount the secret as a volume within the application.
                          Secret Manager is a service that allows you to securely store and manage sensitive data such as passwords, API keys, certificates, and tokens. You can use Secret Manager to rotate your secrets automatically or manually, and access them from your Cloud Run applications1.
                          There are two ways to use secrets from Secret Manager in Cloud Run:
                          As environment variables: You can set environment variables that point to secrets in Secret Manager. Cloud Run will resolve the secrets at runtime and inject them into the environment of your application. However, this method has some limitations, such as:
                          The environment variables are cached for up to 10 minutes, so you may not get the latest version of the secret immediately.
                          The environment variables are visible in plain text in the Cloud Console and the Cloud SDK, which may expose sensitive information.
                          The environment variables are limited to 4 KB of data, which may not be enough for some secrets.2 As file system volumes: You can mount secrets from Secret Manager as files in a volume within your application. Cloud Run will create a tmpfs volume and write the secrets as files in it. This method has some advantages, such as:
                          The files are updated every 30 seconds, so you can get the latest version of the secret faster.
                          The files are not visible in the Cloud Console or the Cloud SDK, which provides better security.
                          The files can store up to 64 KB of data, which allows for larger secrets.3 Therefore, for your use case, it is better to use the second method and mount the secret as a file system volume within your application. This way, you can ensure that your application has the latest password, and you can deploy it with no downtime.
                          To mount a secret as a file system volume in Cloud Run, you can use the following command:
                          gcloud beta run deploy SERVICE --image IMAGE_URL --update-secrets=/path/to/file=secretName:version where:
                          SERVICE is the name of your Cloud Run service.
                          IMAGE_URL is the URL of your container image.
                          /path/to/file is the path where you want to mount the secret file in your application.
                          secretName is the name of your secret in Secret Manager.
                          You can also use the Cloud Console to mount secrets as file system volumes. For more details, see Mounting secrets from Secret Manager.
                          Reference:
                          1: Overview | Secret Manager Documentation | Google Cloud
                          2: Using secrets as environment variables | Cloud Run Documentation | Google Cloud
                          3: Mounting secrets from Secret Manager | Cloud Run Documentation | Google Cloud


                          NEW QUESTION # 208
                          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: C

                          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 # 209
                          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?

                          Answer: D

                          Explanation:
                          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 ManagedService for Prometheus, you can avoid managing and scaling your own Prometheus servers and focus on your application performance.


                          NEW QUESTION # 210
                          ......

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