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

Section Objectives 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 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. 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

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 (Q73-Q78):

NEW QUESTION # 73

Your team uses Cloud Build for all CI/CO pipelines. You want to use the kubectl builder for Cloud Build to deploy new images to Google Kubernetes Engine (GKE). You need to authenticate to GKE while minimizing development effort. What should you do?

A. Create a new service account with the Container Developer role and use it to run Cloud Build.
B. Specify the Container Developer role for Cloud Build in the cloudbuild.yaml file.
C. Assign the Container Developer role to the Cloud Build service account.
D. Create a separate step in Cloud Build to retrieve service account credentials and pass these to kubectl.

Answer: A

NEW QUESTION # 74

Your company processes IOT data at scale by using Pub/Sub, App Engine standard environment, and an application written in GO. You noticed that the performance inconsistently degrades at peak load. You could not reproduce this issue on your workstation. You need to continuously monitor the application in production to identify slow paths in the code. You want to minimize performance impact and management overhead. What should you do?

A. Install a continuous profiling tool into Compute Engine. Configure the application to send profiling data to the tool.
B. Use Cloud Monitoring to assess the App Engine CPU utilization metric.
C. Periodically run the go tool pprof command against the application instance. Analyze the results by using flame graphs.
D. Configure Cloud Profiler, and initialize the cloud.go@gle.com/go/profiler library in the application.

Answer: D

Explanation:

The correct answer is C. Configure Cloud Profiler, and initialize the cloud.google.com/go/profiler library in the application.

According to the Google Cloud documentation, Cloud Profiler is a statistical, low-overhead profiler that continuously gathers CPU usage and memory-allocation information from your production applications1. Cloud Profiler can help you identify slow paths in your code and optimize the performance of your applications. Cloud Profiler supports applications written in Go that run on App Engine standard environment2. To use Cloud Profiler, you need to configure it in your Google Cloud project and initialize the cloud.google.com/go/profiler library in your application code3. You can then use the Cloud Profiler interface to analyze the profiling data and visualize the results by using flame graphs4. Cloud Profiler has minimal performance impact and management overhead, as it only samples a small fraction of the application activity and does not require any additional infrastructure or agents.

The other options are incorrect because they do not meet the requirements of minimizing performance impact and management overhead. Option A is incorrect because it requires installing a continuous profiling tool into Compute Engine, which is an additional infrastructure that needs to be managed and maintained. Option B is incorrect because it requires periodically running the go tool pprof command against the application instance, which is a manual and disruptive process that can affect the application performance. Option D is incorrect because it only uses Cloud Monitoring to assess the App Engine CPU utilization metric, which is not enough to identify slow paths in the code or optimize the application performance.

Reference:

Cloud Profiler documentation, Overview. Profiling Go applications, Supported environments. Profiling Go applications, Using Cloud Profiler. Analyzing data, Analyzing data.

NEW QUESTION # 75

You need to build a CI/CD pipeline for a containerized application in Google Cloud Your development team uses a central Git repository for trunk-based development You want to run all your tests in the pipeline for any new versions of the application to improve the quality What should you do?

A. 1. Trigger Cloud Build to run unit tests when the code is pushed If all unit tests are successful, build and push the application container to a central registry.
2. Trigger Cloud Build to deploy the container to a testing environment, and run integration tests and acceptance tests
3. If all tests are successful the pipeline deploys the application to the production environment and runs smoke tests
B. 1. Trigger Cloud Build to build the application container and run unit tests with the container
2. If unit tests are successful, deploy the application container to a testing environment, and run integration tests
3. If the integration tests are successful the pipeline deploys the application container to the production environment After that, run acceptance tests
C. 1. Install a Git hook to require developers to run unit tests before pushing the code to a central repository If all tests are successful build a container
2. Trigger Cloud Build to deploy the application container to a testing environment, and run integration tests and acceptance tests
3. If all tests are successful tag the code as production ready Trigger Cloud Build to build and deploy the application container to the production environment
D. 1. Install a Git hook to require developers to run unit tests before pushing the code to a central repository
2. Trigger Cloud Build to build the application container Deploy the application container to a testing environment, and run integration tests
3. If the integration tests are successful deploy the application container to your production environment. and run acceptance tests

Answer: A

Explanation:

The best option for building a CI/CD pipeline for a containerized application in Google Cloud is to trigger Cloud Build to run unit tests when the code is pushed, if all unit tests are successful, build and push the application container to a central registry, trigger Cloud Build to deploy the container to a testing environment, and run integration tests and acceptance tests, and if all tests are successful, the pipeline deploys the application to the production environment and runs smoke tests. This option follows the best practices for CI/CD pipelines, such as running tests at different stages of the pipeline, using a central registry for storing and managing containers, deploying to different environments, and using Cloud Build as a unified tool for building, testing, and deploying.

NEW QUESTION # 76

You have a set of applications running on a Google Kubernetes Engine (GKE) cluster, and you are using Stackdriver Kubernetes Engine Monitoring. You are bringing a new containerized application required by your company into production. This application is written by a third party and cannot be modified or reconfigured. The application writes its log information to /var/log/app_messages.log, and you want to send these log entries to Stackdriver Logging. What should you do?

A. Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.
B. Write a script to tail the log file within the pod and write entries to standard output. Run the script as a sidecar container with the application's pod. Configure a shared volume between the containers to allow the script to have read access to /var/log in the application container.
C. Deploy a Fluentd daemonset to GKE. Then create a customized input and output configuration to tail the log file in the application's pods and write to Slackdriver Logging.
D. Install Kubernetes on Google Compute Engine (GCE> and redeploy your applications. Then customize the built-in Stackdriver Logging configuration to tail the log file in the application's pods and write to Stackdriver Logging.

Answer: C

Explanation:

Explanation

https://cloud.google.com/architecture/customizing-stackdriver-logs-fluentd Besides the list of default logs that the Logging agent streams by default, you can customize the Logging agent to send additional logs to Logging or to adjust agent settings by adding input configurations. The configuration definitions in these sections apply to the fluent-plugin-google-cloud output plugin only and specify how logs are transformed and ingested into Cloud Logging.

https://cloud.google.com/logging/docs/agent/logging/configuration#configure

NEW QUESTION # 77

You manage an application that runs in Google Kubernetes Engine (GKE) and uses the blue/green deployment methodology Extracts of the Kubernetes manifests are shown below

The Deployment app-green was updated to use the new version of the application During post-deployment monitoring you notice that the majority of user requests are failing You did not observe this behavior in the testing environment You need to mitigate the incident impact on users and enable the developers to troubleshoot the issue What should you do?

A. Change the selector on the Service app-2vc to app: my-app.
B. Update the Deployment app-blue to use the new version of the application
C. Update the Deployment ape-green to use the previous version of the application
D. Change the selector on the Service app-svc to app: my-app, version: blue

Answer: D

Explanation:

Explanation

The best option for mitigating the incident impact on users and enabling the developers to troubleshoot the issue is to change the selector on the Service app-svc to app: my-app, version: blue. A Service is a resource that defines how to access a set of Pods. A selector is a field that specifies which Pods are selected by the Service. By changing the selector on the Service app-svc to app: my-app, version: blue, you can ensure that the Service only routes traffic to the Pods that have both labels app: my-app and version: blue. These Pods belong to the Deployment app-blue, which uses the previous version of the application. This way, you can mitigate the incident impact on users by switching back to the working version of the application. You can also enable the developers to troubleshoot the issue with the new version of the application in the Deployment app-green without affecting users.

NEW QUESTION # 78

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Professional-Cloud-DevOps-Engineer Practice Test Online - Latest Professional-Cloud-DevOps-Engineer Mock Test

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

100% Pass Quiz 2026 Google Reliable Professional-Cloud-DevOps-Engineer: Google Cloud Certified - Professional Cloud DevOps Engineer Exam Practice Test Online

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q73-Q78):

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