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Microsoft GH-600 Exam Overview:

Certification Vendor:Microsoft / GitHub
Exam Name:Developing in Agentic AI Systems (GH-600) - GitHub Agentic AI Developer
Exam Number:GH-600
Passing Score:700 (scaled score)
Certificate Validity Period:1 year (renewable annually)
Exam Duration:120 minutes
Available Languages:English
Exam Format:Drag and drop, Case studies, Scenario-based questions, Multiple choice, Interactive exam components
Related Certifications:GitHub Copilot Certification
GitHub Advanced Security Certifications
Recommended Training:Microsoft Learn - GH-600 Study Guide
GitHub Advanced Security learning paths
Exam Registration:Official certification page (Microsoft Learn)
Schedule exam (Pearson VUE via Microsoft credentials portal)
Sample Questions:Microsoft GH-600 Sample Questions
Exam Way:Online proctored exam via Pearson VUE; interactive and scenario-based tasks included.
Pre Condition:Experience with SDLC workflows, GitHub Actions, GitHub Copilot, and basic AI agent concepts (tool use, orchestration, and prompt-driven systems).
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/agentic-ai-developer/

Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Implement tool use and environment interaction20–25%- Agent tool configuration
  • 1. Select and configure tools
    • 2. Configure tool permissions and scope
      - MCP server configuration
      • 1. Add MCP servers to agents
        • 2. Configure registries and allow lists
          - Development environment integration
          • 1. Enable CI-based agent execution
            • 2. Scope agents to repositories or branches
              • 3. Enable autonomous actions (PRs, branches)
                - Safe execution and error handling
                • 1. Escalation paths and traceability
                  • 2. Retries and rollback strategies
                    Orchestrate multi-agent coordination15–20%- Multi-agent workflows
                    • 1. Coordinate parallel agent execution
                      • 2. Resolve conflicts and overlaps
                        - Observability and auditability
                        • 1. Document agent handoffs and decisions
                          • 2. Generate logs and artifacts for review
                            - Lifecycle management
                            • 1. Add/replace/retire agents safely
                              - Failure handling and recovery
                              • 1. Implement rollback and recovery patterns
                                • 2. Detect stalled or degraded agents
                                  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
                                              Evaluation, error analysis, and tuning15–20%- Define evaluation criteria
                                              • 1. Define success metrics and constraints
                                                • 2. Generate automated evaluation signals
                                                  - Failure analysis
                                                  • 1. Analyze logs, traces, and artifacts
                                                    • 2. Classify reasoning, tool, and context errors
                                                      - Tuning agent behavior
                                                      • 1. Optimize memory usage and constraints
                                                        • 2. Refine prompts, tools, and workflows
                                                          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
                                                                  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. 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. Validate structured agent plans
                                                                                  • 3. Separate planning and execution phases

                                                                                    Microsoft GitHub Agentic AI Developer Sample Questions:

                                                                                    Question #1

                                                                                    You have a GitHub repository.
                                                                                    You use the GitHub Copilot CLI to run an agentic workflow from the terminal.
                                                                                    During execution, the conversation history approaches the context limit. The agent cannot continue the current session unless the amount of retained context is reduced.
                                                                                    You need to continue the current session without losing all prior progress.
                                                                                    Which Copilot CLI slash command should you run?

                                                                                    • A. /compact
                                                                                    • B. /context
                                                                                    • C. /yolo
                                                                                    • D. /clear
                                                                                    Reveal Solution  Discussion  0

                                                                                    Correct Answer: A  🗳️

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                                                                                    Question #2

                                                                                    You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
                                                                                    Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
                                                                                    You review the session log and the pull request.
                                                                                    What are two automated security scanning signals available for the pull request? Each correct answer presents a complete solution.
                                                                                    NOTE: Each correct selection is worth one point.

                                                                                    • A. The median time to merge metric reported on the Copilot usage metrics dashboard
                                                                                    • B. The detection of hardcoded secrets, such as API keys and tokens
                                                                                    • C. CodeQL findings that identify security vulnerabilities in the generated code
                                                                                    • D. Linting errors from the repository's CI pipeline configured in GitHub Actions
                                                                                    • E. Dependency alerts that identify vulnerable package versions in the repository
                                                                                    Reveal Solution  Discussion  0

                                                                                    Correct Answer: B,C  🗳️

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                                                                                    Question #3

                                                                                    You have multiple GitHub Copilot coding agents that run tasks concurrently.
                                                                                    You are monitoring the agents from the terminal by using the GitHub CLI.
                                                                                    An agent appears stalled.
                                                                                    You need to live stream the session log output.
                                                                                    What should you do?

                                                                                    • A. Run gh agent-task list and specify the --web parameter.
                                                                                    • B. Run gh agent-task view and specify the --log and --follow parameters.
                                                                                    • C. Run gh agent-task view and specify the --log and --jq parameters.
                                                                                    • D. Run gh agent-task list and specify the --status and --jq parameters.
                                                                                    Reveal Solution  Discussion  0

                                                                                    Correct Answer: B  🗳️

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                                                                                    Question #4

                                                                                    You have a GitHub repository that runs an agentic software development lifecycle workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
                                                                                    You need to coordinate multiple specialized agents so that analysis and risk review run in parallel and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors and then fan in the results to a final executor.
                                                                                    How should you complete the workflow definition? To answer, drag the appropriate values to the correct targets.

                                                                                    Reveal Solution  Discussion  0

                                                                                    Correct Answer:

                                                                                    Question #5

                                                                                    You have a multi-agent GitHub Actions workflow that uploads review artifacts for each run.
                                                                                    You discover that some workflow run artifacts are being deleted manually.
                                                                                    You need to use your organization's audit log data to identify which user deleted the artifacts.
                                                                                    Which audit log search filter should you use?

                                                                                    • A. repo:<org>/<repo>
                                                                                    • B. action:artifact.destroy
                                                                                    • C. action:workflows.run
                                                                                    • D. operation:remove
                                                                                    Reveal Solution  Discussion  0

                                                                                    Correct Answer: B  🗳️

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