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Updated: Aug 08, 2026

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Google Professional-Cloud-DevOps-Engineer日本語 Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Certified - Professional Cloud DevOps Engineer Exam
Exam Number:Professional-Cloud-DevOps-Engineer
Available Languages:English, Japanese, Spanish, Portuguese
Related Certifications:Google Cloud Associate Cloud Engineer
Google Cloud Professional Cloud Architect
Google Cloud Professional Cloud Developer
Real Exam Qty:50
Exam Format:Multiple Choice, Multiple Select
Exam Duration:120 minutes
Exam Price:$200 USD
Passing Score:70%
Certificate Validity Period:2 years
Sample Questions:Google Professional-Cloud-DevOps-Engineer日本語 Sample Questions
Exam Way:Online proctored exam available through Pearson VUE or online proctoring. In-person testing at Kryterion test centers also available.
Pre Condition:Recommended: Associate Cloud Engineer certification and 3+ years industry experience including 1+ years Google Cloud experience. No mandatory prerequisites required.
Official Syllabus URL:https://cloud.google.com/certification/cloud-devops-engineer

Topics of Google Professional Cloud DevOps Engineer Exam

Candidates must know the exam topics before they start preparation because it will help them in hitting the core. Our Professional Cloud DevOps Engineer Dumps will include the following topics:

Applying site reliability engineering principles to a service

  • Toil automation
  • Define SLOs and understand SLAs
  • Balance change, velocity, and reliability of the service:
  • Construct feedback loops to decide what to build next
  • Agree to consequences of not meeting the error budget
  • Discover SLIs (availability, latency, etc.)
  • Manage a service (e.g., introduce a new service, deploy it, maintain and retire it)
  • 1.2 Manage service life cycle:

Building and implementing CI/CD pipelines for a service

  • Deployment strategies with Cloud Build, Spinnaker
  • Artifact versioning strategy with Cloud Build, Container Registry
  • Artifact repositories with Container Registry
  • Testing a new version with Spinnaker
  • Immutable artifacts with Container Registry
  • Design CI/CD pipelines:
  • Deployment to hybrid and multi-cloud environments with Anthos, Spinnaker, Kubernetes
  • CI/CD pipeline triggers with Cloud Source Repositories, Cloud Build GitHub App, Cloud Pub/Sub
  • Configure deployment processes (e.g., approval flows)

Implementing service monitoring strategies

  • Selecting the options for logging export
  • Implementing logs-based metrics
  • Set ACL to restrict export configuration with IAM, Stackdriver Logging
  • Use metric explorer for ad hoc metric analysis
  • Set ACL to allow metric writing for custom metrics with IAM, Stackdriver Monitoring
  • Sending application logs directly to Stackdriver API with Stackdriver Logging
  • Viewing export logs in Cloud Storage and BigQuery
  • Viewing logs in the GCP Console
  • Using basic vs. advanced logging filters
  • Implementing a project-level / org-level export
  • Set ACL to restrict access to audit logs with IAM, Stackdriver Logging
  • Sending logs to an external logging platform
  • Understanding the logging exclusion vs. logging export
  • Collecting metrics from Compute Engine
  • Collecting GKE/Kubernetes metrics
  • Manage application logs:
  • Collecting logs from Compute Engine, GKE with Stackdriver Logging, Fluentd
  • Collecting third-party and structured logs with Stackdriver Logging, Fluentd
  • Enabling VPC flow logs
  • Enabling data access logs (e.g., Cloud Audit Logs)

Optimizing service performance

  • Utilize Stackdriver to identify cloud resource utilization
  • Utilize Stackdriver Trace/Profiler to profile performance characteristics
  • Consider network pricing
  • Evaluate and understand user impact (Stackdriver Service Monitoring for App Engine, Istio)
  • Manage preemptible VMs
  • Troubleshoot issues with the image/OS
  • Troubleshoot network issues (e.g., VPC flow logs, firewall logs, latency, view network details)
  • Identify resource utilization levels
  • Interpret service mesh telemetry
  • Identify service performance issues:
  • TCO considerations
  • identify resource costs
  • Work with committed-use discounts
  • Develop a plan to optimize areas of greatest cost or lowest utilization

Managing service incidents

  • Provide regular status updates, internal and external
  • Scaling response team and delegation
  • Coordinate roles and implement communication channels during a service incident:
  • Identify alternatives to mitigate the issue
  • Avoid exhaustion/burnout
  • Evaluate symptoms against probable causes; the rank probability of cause based on observed behavior
  • Identify probable causes of service failure
  • Define roles (incident commander, communication lead, operations lead)
  • Rotate/hand over roles
  • Handle requests for impact assessment
  • Establish communications channels (email, IRC, Hangouts, Slack, phone, etc.)
  • Perform an investigation to isolate the most likely actual cause
  • Manage stakeholder relationships
  • Record major changes in incident state (When mitigated? When all clear? etc.)

How much Google Professional Cloud DevOps Engineer Exam cost

Google Professional Cloud DevOps Engineer exam cost is USD 200, for more information related to the Google Professional Cloud DevOps Engineer exam, please visit Google Website. Additional costs of Professional Cloud DevOps Engineer exam dumps pdf and Professional Cloud DevOps Engineer practice test are not included in this price.

Reference: https://cloud.google.com/certification/cloud-devops-engineer

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

SectionWeightObjectives
CI/CD Pipeline Development26%- CI/CD best practices
  • 1. Implementing shift-left testing
  • 2. Managing secrets in CI/CD pipelines
  • 3. Configuring quality gates and code coverage
- Containerization and Docker
  • 1. Managing container registries
  • 2. Implementing container security best practices
  • 3. Creating optimized Docker images (multi-stage builds)
- Designing and implementing CI/CD pipelines
  • 1. Configuring build triggers and webhooks
  • 2. Implementing build automation scripts
  • 3. Configuring build caching strategies
  • 4. Setting up artifact management with Artifact Registry
  • 5. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.)
Cloud Infrastructure Automation24%- Deployment strategies
  • 1. Rolling updates with Kubernetes
  • 2. Canary releases and progressive rollouts
  • 3. Feature flags with Firebase Remote Config or LaunchDarkly
  • 4. Blue-green deployments
- 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. Implementing configuration drift detection
  • 3. Using Ansible for configuration management
Monitoring, Logging, and Debugging12%- Cloud Operations Suite
  • 1. Implementing Cloud Logging with log sinks
  • 2. Configuring Cloud Monitoring metrics
  • 3. Creating uptime checks and alerts
- Log management
  • 1. Implementing log-based metrics
  • 2. Structuring logs for efficient querying
  • 3. Configuring log retention policies
- Debugging and troubleshooting
  • 1. Analyzing performance profiles with Cloud Profiler
  • 2. Debugging with Cloud Trace distributed tracing
  • 3. Using Cloud Debugger and Error Reporting
Microservices Architecture14%- 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
- Service mesh and networking
  • 1. Implementing service-to-service authentication
  • 2. Configuring traffic management and load balancing
  • 3. Implementing Anthos Service Mesh / Istio
- API management
  • 1. Implementing API versioning strategies
  • 2. Exposing APIs with Cloud Endpoints / Apigee
Reliability and Site Reliability Engineering (SRE)24%- SLOs, SLIs, and SLAs
  • 1. Setting appropriate Service Level Agreements
  • 2. Creating and interpreting Service Level Indicators
  • 3. Error budget policies and management
  • 4. Defining and implementing Service Level Objectives
- Incident management
  • 1. Configuring automated incident response
  • 2. Post-incident reviews and blameless postmortems
  • 3. Implementing on-call procedures
- Monitoring and observability
  • 1. Setting up alerting policies and incident management
  • 2. Implementing distributed tracing with Cloud Trace
  • 3. Configuring Cloud Monitoring and Logging
  • 4. Creating dashboards for service health visibility

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