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Microsoft GH-600 Exam Overview:

Certification Vendor:Microsoft / GitHub
Exam Name:Developing in Agentic AI Systems (GH-600) - GitHub Agentic AI Developer
Exam Number:GH-600
Certificate Validity Period:1 year (renewable annually)
Exam Duration:120 minutes
Exam Format:Multiple choice, Scenario-based questions, Drag and drop, Case studies, Interactive exam components
Passing Score:700 (scaled score)
Available Languages:English
Related Certifications:GitHub Copilot Certification
GitHub Advanced Security Certifications
Recommended Training:Microsoft Learn - GH-600 Study Guide
GitHub Advanced Security learning paths
Exam Registration:Schedule exam (Pearson VUE via Microsoft credentials portal)
Official certification page (Microsoft Learn)
Sample Questions:Microsoft GH-600 Sample Questions
Exam Way:Online proctored exam via Pearson VUE; interactive and scenario-based tasks included.
Pre Condition:Experience with SDLC workflows, GitHub Actions, GitHub Copilot, and basic AI agent concepts (tool use, orchestration, and prompt-driven systems).
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/agentic-ai-developer/

Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Implement tool use and environment interaction20–25%- Agent tool configuration
  • 1. Configure tool permissions and scope
    • 2. Select and configure tools
      - MCP server configuration
      • 1. Configure registries and allow lists
        • 2. Add MCP servers to agents
          - Safe execution and error handling
          • 1. Escalation paths and traceability
            • 2. Retries and rollback strategies
              - Development environment integration
              • 1. Scope agents to repositories or branches
                • 2. Enable CI-based agent execution
                  • 3. Enable autonomous actions (PRs, branches)
                    Evaluation, error analysis, and tuning15–20%- Failure analysis
                    • 1. Classify reasoning, tool, and context errors
                      • 2. Analyze logs, traces, and artifacts
                        - Define evaluation criteria
                        • 1. Define success metrics and constraints
                          • 2. Generate automated evaluation signals
                            - Tuning agent behavior
                            • 1. Refine prompts, tools, and workflows
                              • 2. Optimize memory usage and constraints
                                Prepare agent architecture and SDLC processes15–20%- Observability and control
                                • 1. Produce inspectable artifacts in GitHub
                                  • 2. Define autonomy levels and guardrails
                                    • 3. Enable human-in-the-loop controls
                                      - Planning vs execution boundaries
                                      • 1. Separate planning and execution phases
                                        • 2. Validate structured agent plans
                                          • 3. Prevent execution before approval
                                            - Integrate agents into SDLC workflows
                                            • 1. Define agent steps in SDLC
                                              • 2. Identify and mitigate agent anti-patterns
                                                • 3. Define inputs, outputs, and success criteria
                                                  Orchestrate multi-agent coordination15–20%- Lifecycle management
                                                  • 1. Add/replace/retire agents safely
                                                    - Observability and auditability
                                                    • 1. Document agent handoffs and decisions
                                                      • 2. Generate logs and artifacts for review
                                                        - Failure handling and recovery
                                                        • 1. Detect stalled or degraded agents
                                                          • 2. Implement rollback and recovery patterns
                                                            - Multi-agent workflows
                                                            • 1. Coordinate parallel agent execution
                                                              • 2. Resolve conflicts and overlaps
                                                                Implement guardrails and accountability10–15%- Autonomy and risk levels
                                                                • 1. Classify agent actions by risk
                                                                  • 2. Assign autonomy levels with compliance constraints
                                                                    - Guardrails and human-in-the-loop
                                                                    • 1. Enforce least-privilege execution
                                                                      • 2. Require approvals for sensitive actions
                                                                        Manage memory, state, and execution10–15%- Cross-tool continuity
                                                                        • 1. Prevent stale or conflicting context
                                                                          • 2. Share state across tools and environments
                                                                            - Agent memory strategies
                                                                            • 1. Short-term vs long-term memory selection
                                                                              • 2. Memory scoping and expiration rules
                                                                                - State persistence and drift control
                                                                                • 1. Detect and correct context drift
                                                                                  • 2. Persist task progress as artifacts

                                                                                    Microsoft GitHub Agentic AI Developer Sample Questions:

                                                                                    1. You need finer control, selecting specific files and describing precise natural-language changes to apply, rather than letting the agent decide the full scope of changes. Which Copilot Chat mode should you use?

                                                                                    A) Edit mode
                                                                                    B) Agent mode
                                                                                    C) Plan mode
                                                                                    D) Ask mode


                                                                                    2. During an agentic session, you want to see exactly how many tokens are being consumed by the system prompt, tools, and message history before deciding whether to compact. Which command should you run?

                                                                                    A) /context
                                                                                    B) /diff
                                                                                    C) /usage
                                                                                    D) /tokens


                                                                                    3. You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?

                                                                                    A) Add clear, reproducible steps and expected behavior to the issue
                                                                                    B) Increase MCP server rate limits
                                                                                    C) Enable Copilot memory
                                                                                    D) Switch the agent to --allow-all mode


                                                                                    4. Drag and Drop Question
                                                                                    You have a GitHub repository that uses the GitHub Copilot CLI to run autonomous tasks.
                                                                                    You need to validate each generated command before it runs. Any commands that attempt to modify paths outside the repository must be blocked.
                                                                                    How should you complete the YAML? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
                                                                                    NOTE: Each correct selection is worth one point.


                                                                                    5. You have a GitHub repository that uses GitHub Copilot code review on pull requests.
                                                                                    You plan to add repository-wide code review guidance that will apply to all files.
                                                                                    You need Copilot code review to consistently apply the guidance during pull request reviews.
                                                                                    What should you do?

                                                                                    A) Create a .github/copilot-instructions.md file.
                                                                                    B) Create a .github/instructions/copilot.instructions.md file.
                                                                                    C) Update the instructions in the .github/agents/*.agent.md files.
                                                                                    D) Modify the .github/pull_request_template.md file.


                                                                                    Solutions:

                                                                                    Question # 1
                                                                                    Answer: A
                                                                                    Question # 2
                                                                                    Answer: A
                                                                                    Question # 3
                                                                                    Answer: A
                                                                                    Question # 4
                                                                                    Answer: Only visible for members
                                                                                    Question # 5
                                                                                    Answer: B

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