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Microsoft GitHub Agentic AI Developer : GH-600

GH-600 Exam Simulator
  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Sep 12, 2026
  • Q & A: 111 Questions and Answers

  • Microsoft GH-600 Q&A - in .pdf

  • Printable Microsoft GH-600 PDF Format. It is an electronic file format regardless of the operating system platform.
  • PDF Version Price: $59.99
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Microsoft GH-600 exam simulator

Microsoft GH-600 Exam Syllabus Topics:

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

                                                                                    Microsoft GitHub Agentic AI Developer Sample Questions:

                                                                                    Question #1

                                                                                    You need to make changes to repo1 to support the planned changes for the developers.
                                                                                    What should you modify?

                                                                                    • A. .vscode/settings.json
                                                                                    • B. .github/agents/*.agent.md
                                                                                    • C. <project>/.mcp/server.json
                                                                                    • D. .vscode/mcp.json
                                                                                    Answer: B

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                                                                                    Question #2

                                                                                    You have a GitHub Enterprise organization that has Copilot memory enabled.
                                                                                    You create a new repository.
                                                                                    What are two ways that memories will be deleted from the repository? Each correct answer presents a complete solution.
                                                                                    NOTE: Each correct selection is worth one point.

                                                                                    • A. when the code that created the memory is deleted
                                                                                    • B. when pull requests are merged
                                                                                    • C. automatically after 28 days
                                                                                    • D. when the repository is archived
                                                                                    • E. manually by repository members
                                                                                    Answer: C,E

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                                                                                    Question #3

                                                                                    You have a GitHub repository that uses the GitHub Copilot coding agent to run agent plans for assigned issues.
                                                                                    You discover that some issues contain ambiguous implementation steps.
                                                                                    You need to reduce the likelihood that Copilot executes plans based on ambiguous implementation steps.
                                                                                    What should you do?

                                                                                    • A. Add comments to the issues after assigning the issues to the agent.
                                                                                    • B. Mention @copilot in a pull request comment to request changes after the plan runs.
                                                                                    • C. Add a copilot-setup-steps.yml file to preinstall dependencies in the agent environment.
                                                                                    • D. Add acceptance criteria and directions to the issues before assigning the issues to the agent.
                                                                                    Answer: D

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                                                                                    Question #4

                                                                                    You have a repository that uses a GitHub Actions workflow to run an agent-driven change plan as part of a CI pipeline. The workflow generates an artifact named plan.json that includes a field named risk. Risk has possible values of low, medium, or high.
                                                                                    You need to ensure that a human must confirm the execution of the workflow when risk is medium or high. The workflow must proceed automatically only when risk is low.
                                                                                    How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

                                                                                    Answer:

                                                                                    Question #5

                                                                                    You have a GitHub repository that uses the GitHub Copilot coding agent. The repository uses strict branch protections on the main branch.
                                                                                    You maintain a long-lived branch named release/1.4 that has different dependencies and CI checks.
                                                                                    You need the agent to create a hotfix that is implemented and validated against the release/1.4 branch. The solution must prevent the agent from basing its work on main.
                                                                                    What should you do?

                                                                                    • A. In .github/copilot-instructions.md, instruct the agent to always check out release/1.4 instead of main when creating hotfixes.
                                                                                    • B. Select the repository and the release/1.4 branch in the Copilot agents UI before you run the agent task.
                                                                                    • C. Require human reviews on changes to main.
                                                                                    • D. Add concurrency: release/1.4 to .github/workflows/copilot-setup-steps.yml.
                                                                                    Answer: B

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