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

Section	Weight	Objectives
Cloud Infrastructure Automation	24%	- Infrastructure as Code (IaC) 1. Managing infrastructure modules and state 2. Implementing with Terraform 3. Implementing immutable infrastructure patterns - Configuration management 1. Managing secrets with Secret Manager 2. Using Ansible for configuration management 3. Implementing configuration drift detection - Deployment strategies 1. Feature flags with Firebase Remote Config or LaunchDarkly 2. Blue-green deployments 3. Rolling updates with Kubernetes 4. Canary releases and progressive rollouts
Monitoring, Logging, and Debugging	12%	- Log management 1. Implementing log-based metrics 2. Structuring logs for efficient querying 3. Configuring log retention policies - Cloud Operations Suite 1. Implementing Cloud Logging with log sinks 2. Configuring Cloud Monitoring metrics 3. Creating uptime checks and alerts - Debugging and troubleshooting 1. Analyzing performance profiles with Cloud Profiler 2. Debugging with Cloud Trace distributed tracing 3. Using Cloud Debugger and Error Reporting
Reliability and Site Reliability Engineering (SRE)	24%	- SLOs, SLIs, and SLAs 1. Error budget policies and management 2. Setting appropriate Service Level Agreements 3. Defining and implementing Service Level Objectives 4. Creating and interpreting Service Level Indicators - Incident management 1. Implementing on-call procedures 2. Post-incident reviews and blameless postmortems 3. Configuring automated incident response - Monitoring and observability 1. Setting up alerting policies and incident management 2. Creating dashboards for service health visibility 3. Implementing distributed tracing with Cloud Trace 4. Configuring Cloud Monitoring and Logging
CI/CD Pipeline Development	26%	- Designing and implementing CI/CD pipelines 1. Configuring build triggers and webhooks 2. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.) 3. Setting up artifact management with Artifact Registry 4. Configuring build caching strategies 5. Implementing build automation scripts - CI/CD best practices 1. Configuring quality gates and code coverage 2. Implementing shift-left testing 3. Managing secrets in CI/CD pipelines - Containerization and Docker 1. Implementing container security best practices 2. Creating optimized Docker images (multi-stage builds) 3. Managing container registries
Microservices Architecture	14%	- Container orchestration with GKE 1. Configuring node pools and auto-scaling 2. Implementing workload deployment and scaling 3. Designing Kubernetes cluster architectures 4. Managing pod lifecycle and resource quotas - API management 1. Exposing APIs with Cloud Endpoints / Apigee 2. Implementing API versioning strategies - Service mesh and networking 1. Implementing service-to-service authentication 2. Configuring traffic management and load balancing 3. Implementing Anthos Service Mesh / Istio

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q168-Q173):

NEW QUESTION # 168
You are deploying a Cloud Build job that deploys Terraform code when a Git branch is updated. While testing, you noticed that the job fails. You see the following error in the build logs:
Initializing the backend. ..
Error: Failed to get existing workspaces : querying Cloud Storage failed: googleapi : Error
403
You need to resolve the issue by following Google-recommended practices. What should you do?

A. Grant the roles/ storage. objectAdmin Identity and Access Management (IAM) role to the Cloud Build service account on the state file bucket.
B. Create a storage bucket with the name specified in the Terraform configuration.
C. Grant the roles/ owner Identity and Access Management (IAM) role to the Cloud Build service account on the project.
D. Change the Terraform code to use local state.

Answer: A

NEW QUESTION # 169
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. Ask the pull request reviewers to run the integration tests before approving the code.
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. Use Cloud Build to run tests in a specific folder. Trigger Cloud Build for every GitHub pull request.

Answer: D

Explanation:
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'
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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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