Microsoft GitHub Agentic AI Developer GH-600 Certified Exam Dumps

GH-600 Exam Dumps

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Certification Provider: Microsoft
Exam Code / Number: GH-600
Exam Name: GitHub Agentic AI Developer
Exam Questions: 85
Last Updated: Aug 28, 2026
Corresponding Certification: GitHub Administrator

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Microsoft GH-600 Exam Syllabus Topics:

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


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