Microsoft GH-600 exam dumps : GitHub Agentic AI Developer

  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Jul 30, 2026     Q & A: 0 Questions and Answers

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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
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
              Orchestrate multi-agent coordination15–20%- Lifecycle management
              • 1. Add/replace/retire agents safely
                - Failure handling and recovery
                • 1. Detect stalled or degraded agents
                  • 2. Implement rollback and recovery patterns
                    - 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
                            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
                                  - Planning vs execution boundaries
                                  • 1. Prevent execution before approval
                                    • 2. Separate planning and execution phases
                                      • 3. Validate structured agent plans
                                        - 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%- Guardrails and human-in-the-loop
                                              • 1. Enforce least-privilege execution
                                                • 2. Require approvals for sensitive actions
                                                  - Autonomy and risk levels
                                                  • 1. Assign autonomy levels with compliance constraints
                                                    • 2. Classify agent actions by risk
                                                      Implement tool use and environment interaction20–25%- Development environment integration
                                                      • 1. Scope agents to repositories or branches
                                                        • 2. Enable CI-based agent execution
                                                          • 3. Enable autonomous actions (PRs, branches)
                                                            - Agent tool configuration
                                                            • 1. Select and configure tools
                                                              • 2. Configure tool permissions and scope
                                                                - MCP server configuration
                                                                • 1. Configure registries and allow lists
                                                                  • 2. Add MCP servers to agents
                                                                    - Safe execution and error handling
                                                                    • 1. Retries and rollback strategies
                                                                      • 2. Escalation paths and traceability
                                                                        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. Short-term vs long-term memory selection
                                                                              • 2. Memory scoping and expiration rules
                                                                                - Cross-tool continuity
                                                                                • 1. Prevent stale or conflicting context
                                                                                  • 2. Share state across tools and environments

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