Google Professional-Cloud-DevOps-Engineer日本語 : Google Cloud Certified - Professional Cloud DevOps Engineer Exam (Professional-Cloud-DevOps-Engineer日本語版)

Professional-Cloud-DevOps-Engineer日本語 real exams

Exam Code: Professional-Cloud-DevOps-Engineer-JPN

Exam Name: Google Cloud Certified - Professional Cloud DevOps Engineer Exam (Professional-Cloud-DevOps-Engineer日本語版)

Updated: Aug 10, 2026

Q & A: 208 Questions and Answers

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Google Professional Cloud DevOps Engineer Certified Professional salary

The average salary of a Google Professional Cloud DevOps Engineer Certified Expert in different regions is as follows:

  • United State: 151,00 USD
  • England: 130,500 POUND
  • Europe: 144,000 EURO
  • India: 25,00,000 INR

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

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Career Prospects

The Google Professional Cloud DevOps Engineer certification demonstrates your ability to efficiently perform the development operations that can balance service reliability and delivery speed. After completing the qualifying exam, you will be proficient in using Google Cloud Platform to build software delivery pipelines, deploy & monitor services, and manage incidents. This expertise will give you access to endless opportunities for career development. Immediately after getting certified, you can start a career as a Cloud DevOps Engineer. Additionally, you can apply for the related job roles, such as a DevOps Infrastructure Engineer, a Cloud Solutions Architect, a GCP Cloud Native Architect, a Google Cloud Platform Data Architect, a GCP Cloud Data Engineer, and more. The average salary that the certificate holders can earn ranges between $137,000 and $180,000 per year.

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

SectionWeightObjectives
Topic 1: Implementing observability and troubleshooting20%- 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
- Optimize observability strategy
  • 1. Scale monitoring for large environments
  • 2. Reduce noise and prioritize signals
  • 3. Integrate observability into development
- Troubleshoot complex issues
  • 1. Isolate problems across layers
  • 2. Analyze performance and latency
  • 3. Trace requests end-to-end
Topic 2: Bootstrapping and maintaining a Google Cloud organization20%- Set up governance and compliance
  • 1. Manage resource quotas and limits
  • 2. Configure audit logging and monitoring
  • 3. Implement organization policies
- Manage configuration and deployment
  • 1. Automate resource provisioning
  • 2. Use Infrastructure as Code tools
  • 3. Enforce consistency across environments
- Design resource hierarchy
  • 1. Plan shared networking and connectivity
  • 2. Organize resources by project/folder/application
  • 3. Define IAM policies and access control
Topic 3: Building and implementing CI/CD pipelines25%- Implement and secure pipelines
  • 1. Enforce security scanning and compliance checks
  • 2. Automate build, test, and deployment stages
  • 3. Manage pipeline secrets and credentials
- Optimize and maintain pipelines
  • 1. Monitor pipeline performance
  • 2. Improve speed and reliability
  • 3. Handle rollbacks and versioning
- Design CI/CD pipelines
  • 1. Support hybrid/multi-cloud deployment
  • 2. Plan pipelines for apps, infrastructure, ML workloads
  • 3. Integrate artifact management
Topic 4: Applying site reliability engineering practices18%- Define reliability goals
  • 1. Establish SLIs, SLOs, SLAs
  • 2. Align reliability with business needs
  • 3. Use error budgets to balance change and stability
- Manage incidents and outages
  • 1. Detect and respond to issues quickly
  • 2. Perform postmortems and root cause analysis
  • 3. Implement improvements to prevent recurrence
- Design for resilience
  • 1. Plan redundancy and fault tolerance
  • 2. Test failure scenarios and recovery
  • 3. Automate remediation and self-healing
Topic 5: Optimizing performance and cost17%- Apply FinOps practices
  • 1. Monitor and analyze cloud costs
  • 2. Use spot instances and committed use discounts
  • 3. Right-size resources and eliminate waste
- Improve system performance
  • 1. Use caching and content delivery
  • 2. Optimize resource allocation
  • 3. Tune applications and infrastructure
- Balance cost, performance, and reliability
  • 1. Optimize over time
  • 2. Make tradeoff decisions based on data
  • 3. Plan capacity and scaling

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