Microsoft GH-300日本語 Exam Details & Actual Exam Questions

  • Exam Code/Number: GH-300日本語
  • Exam Name/Title: GitHub Copilot (GH-300日本語版)
  • Certification Provider: Microsoft
  • Corresponding Certification: GitHub Administrator
  • Exam Questions: 125
  • Updated On: Jul,20 2026
  • Certification Level: Professional

Microsoft GitHub Copilot (GH-300日本語版) Exam Questions

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Microsoft GH-300日本語 Exam Overview:

Certification Vendor:Microsoft / GitHub
Exam Name:GitHub Copilot Exam (GH-300)
Exam Number:GH-300
Passing Score:700/1000
Certificate Validity Period:2 years
Exam Format:Multiple choice, Multiple response, Scenario-based questions, Drag and drop
Exam Price:USD 99
Related Certifications:GitHub Copilot Fundamentals Part 1
GitHub Copilot Fundamentals Part 2
Real Exam Qty:60-75 (approximately 65 scored + unscored items)
Available Languages:English, Spanish, Portuguese (Brazil), Korean, Japanese
Exam Duration:100-120
Recommended Training:GitHub Copilot Fundamentals Part 2
GitHub Copilot Fundamentals Part 1
Exam Registration:Microsoft Learn Certification Page
Pearson VUE Scheduling
Sample Questions:Microsoft GH-300日本語 Sample Questions
Exam Way:Online proctored (Pearson VUE) or test center
Pre Condition:Basic GitHub knowledge and experience using at least one programming language; familiarity with GitHub Copilot recommended but not strictly required
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/github-copilot/

Microsoft GH-300日本語 Exam Syllabus Topics:

SectionWeightObjectives
Improve developer productivity with Copilot10%- Software development enhancement
  • 1. Code generation and refactoring
    • 2. Legacy code modernization
      • 3. Sample data generation
        - Testing and security
        • 1. Security and performance suggestions
          • 2. Unit and integration test generation
            Use GitHub Copilot features30%- IDE integration
            • 1. CLI usage and setup
              • 2. Inline suggestions and chat usage
                • 3. File and repository exclusions
                  - Organization administration
                  • 1. Policy management and feature control
                    • 2. Audit logs and subscription management
                      - Advanced Copilot capabilities
                      • 1. Copilot Spaces and Spark
                        • 2. Agent Mode and Edit Mode
                          • 3. Pull request summaries and code review
                            Prompt engineering and context crafting10%- Prompt design
                            • 1. Zero-shot and few-shot prompting
                              • 2. Prompt structure and context usage
                                - Optimization
                                • 1. Prompt refinement techniques
                                  Understand GitHub Copilot data and architecture15%- System behavior and limitations
                                  • 1. LLM limitations
                                    • 2. Suggestion lifecycle
                                      - Data handling
                                      • 1. Input processing and prompt construction
                                        • 2. Data flow and privacy boundaries
                                          Privacy, exclusions, and safeguards15%- Security safeguards
                                          • 1. Security warnings and mitigations
                                            • 2. Duplication detection
                                              - Privacy configuration
                                              • 1. Content exclusions and repository controls
                                                • 2. Data ownership and usage limitations
                                                  Use GitHub Copilot responsibly15%- Copilot responsible operation
                                                  • 1. Safe usage practices in development workflows
                                                    - Responsible AI principles
                                                    • 1. Ethical AI usage and governance
                                                      • 2. Validation of AI-generated output
                                                        • 3. Risks and limitations of generative AI


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