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Microsoft GitHub Agentic AI Developer : GH-600

GH-600

Exam Code: GH-600

Exam Name: GitHub Agentic AI Developer

Updated: Aug 04, 2026

Q & A: 85 Questions and Answers

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About Microsoft GitHub Agentic AI Developer certification

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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Orchestrate multi-agent coordination15–20%- Multi-agent workflows
  • 1. Resolve conflicts and overlaps
    • 2. Coordinate parallel agent execution
      - Observability and auditability
      • 1. Generate logs and artifacts for review
        • 2. Document agent handoffs and decisions
          - Failure handling and recovery
          • 1. Implement rollback and recovery patterns
            • 2. Detect stalled or degraded agents
              - Lifecycle management
              • 1. Add/replace/retire agents safely
                Topic 2: Implement tool use and environment interaction20–25%- Safe execution and error handling
                • 1. Retries and rollback strategies
                  • 2. Escalation paths and traceability
                    - MCP server configuration
                    • 1. Add MCP servers to agents
                      • 2. Configure registries and allow lists
                        - Development environment integration
                        • 1. Scope agents to repositories or branches
                          • 2. Enable autonomous actions (PRs, branches)
                            • 3. Enable CI-based agent execution
                              - Agent tool configuration
                              • 1. Configure tool permissions and scope
                                • 2. Select and configure tools
                                  Topic 3: Evaluation, error analysis, and tuning15–20%- Define evaluation criteria
                                  • 1. Define success metrics and constraints
                                    • 2. Generate automated evaluation signals
                                      - Tuning agent behavior
                                      • 1. Optimize memory usage and constraints
                                        • 2. Refine prompts, tools, and workflows
                                          - Failure analysis
                                          • 1. Classify reasoning, tool, and context errors
                                            • 2. Analyze logs, traces, and artifacts
                                              Topic 4: Prepare agent architecture and SDLC processes15–20%- Observability and control
                                              • 1. Enable human-in-the-loop controls
                                                • 2. Define autonomy levels and guardrails
                                                  • 3. Produce inspectable artifacts in GitHub
                                                    - Planning vs execution boundaries
                                                    • 1. Prevent execution before approval
                                                      • 2. Separate planning and execution phases
                                                        • 3. Validate structured agent plans
                                                          - 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
                                                                Topic 5: 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. Require approvals for sensitive actions
                                                                      • 2. Enforce least-privilege execution
                                                                        Topic 6: Manage memory, state, and execution10–15%- Cross-tool continuity
                                                                        • 1. Share state across tools and environments
                                                                          • 2. Prevent stale or conflicting context
                                                                            - Agent memory strategies
                                                                            • 1. Memory scoping and expiration rules
                                                                              • 2. Short-term vs long-term memory selection
                                                                                - State persistence and drift control
                                                                                • 1. Persist task progress as artifacts
                                                                                  • 2. Detect and correct context drift

                                                                                    Microsoft GitHub Agentic AI Developer Sample Questions:

                                                                                    1. While using agent mode in VS Code, you want Copilot to run a specific test suite as a validation step after making changes, without manually invoking the terminal each time. What feature enables this?

                                                                                    A) Tool/terminal invocation permissions in agent mode
                                                                                    B) .copilotignore
                                                                                    C) CODEOWNERS
                                                                                    D) MCP server integration


                                                                                    2. Drag and Drop Question
                                                                                    Your team uses a remote GitHub Model Context Protocol (MCP) server for workflows in the software development life cycle (SDLC).
                                                                                    You need to commit a workspace-scoped MCP configuration to ensure that GitHub Copilot can connect to the GitHub-hosted MCP endpoint and authenticate by using a GitHub personal access token (PAT).
                                                                                    How should you complete the mcp.json configuration file? 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.


                                                                                    3. You are analyzing the following agent logs.

                                                                                    You need to classify the error in a report for your company's CTO.
                                                                                    How should you classify the error?

                                                                                    A) Reasoning error
                                                                                    B) Context Issue
                                                                                    C) Tool misuse
                                                                                    D) Network issue


                                                                                    4. You have a GitHub Copilot coding agent named Orchestrator that runs a multi-phase workflow by using the following subagents:
                                                                                    - Explorer gathers context by using read-only tools.
                                                                                    - Modifier applies focused edits.
                                                                                    You are adding a new agent named Summarizer that generates a concise summary after modifications are complete. Summarizer includes the following YAML frontmatter.

                                                                                    The Orchestrator agent lists all three agents in its agents property.
                                                                                    After adding the Summarizer agent, Orchestrator successfully runs Explorer and Modifier but fails to run Summarizer.
                                                                                    What is a possible cause of the failure?

                                                                                    A) Summarizer cannot be invoked as a subagent because disable-model-invocation is set to true.
                                                                                    B) Orchestrator is missing a handoff entry to trigger Summarizer.
                                                                                    C) Summarizer is missing the editing tools required to complete the workflow.
                                                                                    D) Orchestrator cannot call Summarizer because user-invocable is set to false.


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


                                                                                    Solutions:

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

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