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

Section	Weight	Objectives
Topic 1: Building and implementing CI/CD pipelines	25%	- Implement and secure pipelines 1. Automate build, test, and deployment stages 2. Enforce security scanning and compliance checks 3. Manage pipeline secrets and credentials - Design CI/CD pipelines 1. Plan pipelines for apps, infrastructure, ML workloads 2. Integrate artifact management 3. Support hybrid/multi-cloud deployment - Optimize and maintain pipelines 1. Monitor pipeline performance 2. Improve speed and reliability 3. Handle rollbacks and versioning
Topic 2: Bootstrapping and maintaining a Google Cloud organization	20%	- Manage configuration and deployment 1. Automate resource provisioning 2. Use Infrastructure as Code tools 3. Enforce consistency across environments - Design resource hierarchy 1. Define IAM policies and access control 2. Plan shared networking and connectivity 3. Organize resources by project/folder/application - Set up governance and compliance 1. Manage resource quotas and limits 2. Implement organization policies 3. Configure audit logging and monitoring
Topic 3: Applying site reliability engineering practices	18%	- Define reliability goals 1. Use error budgets to balance change and stability 2. Align reliability with business needs 3. Establish SLIs, SLOs, SLAs - Manage incidents and outages 1. Perform postmortems and root cause analysis 2. Detect and respond to issues quickly 3. Implement improvements to prevent recurrence - Design for resilience 1. Plan redundancy and fault tolerance 2. Automate remediation and self-healing 3. Test failure scenarios and recovery
Topic 4: Implementing observability and troubleshooting	20%	- Optimize observability strategy 1. Reduce noise and prioritize signals 2. Scale monitoring for large environments 3. Integrate observability into development - Build monitoring and logging systems 1. Collect and analyze metrics, logs, traces 2. Create dashboards and alerts 3. Use Cloud Monitoring, Cloud Logging, Cloud Trace - Troubleshoot complex issues 1. Trace requests end-to-end 2. Isolate problems across layers 3. Analyze performance and latency
Topic 5: Optimizing performance and cost	17%	- Balance cost, performance, and reliability 1. Plan capacity and scaling 2. Optimize over time 3. Make tradeoff decisions based on data - Improve system performance 1. Use caching and content delivery 2. Optimize resource allocation 3. Tune applications and infrastructure - Apply FinOps practices 1. Use spot instances and committed use discounts 2. Right-size resources and eliminate waste 3. Monitor and analyze cloud costs

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Professional-Cloud-DevOps-Engineer Studienmaterialien: Google Cloud Certified - Professional Cloud DevOps Engineer Exam - Professional-Cloud-DevOps-Engineer Torrent Prüfung & Professional-Cloud-DevOps-Engineer wirkliche Prüfung

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Professional-Cloud-DevOps-Engineer Prüfungsfragen mit Lösungen (Q198-Q203):

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

Antwort: A

Begründung:
Explanation
https://cloud.google.com/build/docs/deploying-builds/deploy-gke
https://cloud.google.com/build/docs/securing-builds/configure-user-specified-service-accounts

199. Frage
You are developing reusable infrastructure as code modules. Each module contains integration tests that launch the module in a test project. You are using GitHub for source control. You need to Continuously test your feature branch and ensure that all code is tested before changes are accepted. You need to implement a solution to automate the integration tests. What should you do?

A. Use Cloud Build to run tests in a specific folder. Trigger Cloud Build for every GitHub pull request.
B. Use Cloud Build to run the tests. Trigger all tests to run after a pull request is merged.
C. Use a Jenkins server for Cl/CD pipelines. Periodically run all tests in the feature branch.
D. Ask the pull request reviewers to run the integration tests before approving the code.

Antwort: A

Begründung:
Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Google Cloud Storage, Cloud Source Repositories, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives1. Cloud Build can also run integration tests as part of your build steps2.
You can use Cloud Build to run tests in a specific folder by specifying the path to the folder in the dir field of your build step3. For example, if you have a folder named tests that contains your integration tests, you can use the following build step to run them:
steps:
- name: 'gcr.io/cloud-builders/go'
args: ['test', '-v']
dir: 'tests'
Copy
You can use Cloud Build to trigger builds for every GitHub pull request by using the Cloud Build GitHub app. The app allows you to automatically build on Git pushes and pull requests and view your build results on GitHub and Google Cloud console4. You can configure the app to run builds on specific branches, tags, or paths5. For example, if you want to run builds on pull requests that target the master branch, you can use the following trigger configuration:
includedFiles:
- '**'
name: 'pull-request-trigger'
github:
name: 'my-repo'
owner: 'my-org'
pullRequest:
branch: '

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