Microsoft GH-600 Exam : GitHub Agentic AI Developer

  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Jul 20, 2026
  • Q & A: 0 Questions and Answers

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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Manage memory, state, and execution10–15%- Agent memory strategies
  • 1. Memory scoping and expiration rules
    • 2. Short-term vs long-term memory selection
      - State persistence and drift control
      • 1. Detect and correct context drift
        • 2. Persist task progress as artifacts
          - Cross-tool continuity
          • 1. Share state across tools and environments
            • 2. Prevent stale or conflicting context
              Evaluation, error analysis, and tuning15–20%- 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
                      - Define evaluation criteria
                      • 1. Define success metrics and constraints
                        • 2. Generate automated evaluation signals
                          Implement tool use and environment interaction20–25%- Agent tool configuration
                          • 1. Configure tool permissions and scope
                            • 2. Select and configure tools
                              - 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
                                            Implement guardrails and accountability10–15%- Autonomy and risk levels
                                            • 1. Assign autonomy levels with compliance constraints
                                              • 2. Classify agent actions by risk
                                                - Guardrails and human-in-the-loop
                                                • 1. Require approvals for sensitive actions
                                                  • 2. Enforce least-privilege execution
                                                    Prepare agent architecture and SDLC processes15–20%- 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
                                                          - Observability and control
                                                          • 1. Define autonomy levels and guardrails
                                                            • 2. Produce inspectable artifacts in GitHub
                                                              • 3. Enable human-in-the-loop controls
                                                                - Planning vs execution boundaries
                                                                • 1. Separate planning and execution phases
                                                                  • 2. Prevent execution before approval
                                                                    • 3. Validate structured agent plans
                                                                      Orchestrate multi-agent coordination15–20%- Lifecycle management
                                                                      • 1. Add/replace/retire agents safely
                                                                        - Failure handling and recovery
                                                                        • 1. Implement rollback and recovery patterns
                                                                          • 2. Detect stalled or degraded agents
                                                                            - Observability and auditability
                                                                            • 1. Generate logs and artifacts for review
                                                                              • 2. Document agent handoffs and decisions
                                                                                - Multi-agent workflows
                                                                                • 1. Resolve conflicts and overlaps
                                                                                  • 2. Coordinate parallel agent execution

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