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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
Certificate Validity Period:1 year (renewable annually)
Exam Duration:120 minutes
Exam Format:Multiple choice, Scenario-based questions, Drag and drop, Case studies, Interactive exam components
Related Certifications:GitHub Copilot Certification
GitHub Advanced Security Certifications
Available Languages:English
Passing Score:700 (scaled score)
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
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. Prevent stale or conflicting context
            • 2. Share state across tools and environments
              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
                          Implement tool use and environment interaction20–25%- 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
                                    - Safe execution and error handling
                                    • 1. Retries and rollback strategies
                                      • 2. Escalation paths and traceability
                                        - Agent tool configuration
                                        • 1. Select and configure tools
                                          • 2. Configure tool permissions and scope
                                            Implement guardrails and accountability10–15%- Guardrails and human-in-the-loop
                                            • 1. Require approvals for sensitive actions
                                              • 2. Enforce least-privilege execution
                                                - Autonomy and risk levels
                                                • 1. Classify agent actions by risk
                                                  • 2. Assign autonomy levels with compliance constraints
                                                    Prepare agent architecture and SDLC processes15–20%- Observability and control
                                                    • 1. Define autonomy levels and guardrails
                                                      • 2. Enable human-in-the-loop controls
                                                        • 3. Produce inspectable artifacts in GitHub
                                                          - Integrate agents into SDLC workflows
                                                          • 1. Identify and mitigate agent anti-patterns
                                                            • 2. Define agent steps in SDLC
                                                              • 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
                                                                      Orchestrate multi-agent coordination15–20%- Observability and auditability
                                                                      • 1. Document agent handoffs and decisions
                                                                        • 2. Generate logs and artifacts for review
                                                                          - Failure handling and recovery
                                                                          • 1. Detect stalled or degraded agents
                                                                            • 2. Implement rollback and recovery patterns
                                                                              - Lifecycle management
                                                                              • 1. Add/replace/retire agents safely
                                                                                - Multi-agent workflows
                                                                                • 1. Coordinate parallel agent execution
                                                                                  • 2. Resolve conflicts and overlaps

                                                                                    Microsoft GitHub Agentic AI Developer Sample Questions:

                                                                                    1. You want the GitHub Copilot coding agent to follow project-specific conventions (coding style, testing requirements, folder structure) on every task it performs in a repository. What should you create?

                                                                                    A) A .copilotignore file
                                                                                    B) A CODEOWNERS file
                                                                                    C) A agents.yml workflow file
                                                                                    D) A copilot-instructions.md file in .github/


                                                                                    2. Drag and Drop Question
                                                                                    You have a GitHub repository that uses a custom GitHub Copilot coding agent defined in the rollout-bot.agent.md file.
                                                                                    You need to update a workflow so that agent-profile changes can be rolled back by reverting a single commit and rerunning the workflow. The workflow must check out the exact commit being deployed and apply the agent profile from the repository at that commit.
                                                                                    How should you complete the workflow? 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. After several long agentic sessions, you notice degraded response quality and want the agent to retain a concise summary of prior progress rather than the full raw history, while continuing without interruption. Which approach addresses this?

                                                                                    A) --allow-all
                                                                                    B) /compact
                                                                                    C) /clear
                                                                                    D) .copilotignore


                                                                                    4. 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 the prior progress.
                                                                                    Which Copilot CLI slash command should you run?

                                                                                    A) /context
                                                                                    B) /yolo
                                                                                    C) /compact
                                                                                    D) /clear


                                                                                    5. Case Study 2
                                                                                    Existing Environment
                                                                                    GitHub Environment
                                                                                    The GitHub environment contains the following:
                                                                                    - Three repositories named product-api, billing-service, and infra-terraform.
                                                                                    - Branch protection on the main branch in all repositories that requires at least one pull request review before merging
                                                                                    - GitHub Actions runners used across all workflows
                                                                                    - A GitHub team named SG_Dev that contains developers
                                                                                    - A GitHub team named SG_Review that contains senior engineers and a security team
                                                                                    - A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
                                                                                    - No custom agent profile is defined.
                                                                                    - A Model Context Protocol (MCP) server named MCP1 is deployed to
                                                                                    https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
                                                                                    MCP1 requires an API key for authentication.
                                                                                    A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
                                                                                    Copilot memory is NOT enabled for the organization.
                                                                                    Problem Statements
                                                                                    Litware identifies the following issues:
                                                                                    - During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope.
                                                                                    - agent1 makes code changes immediately after receiving a task.
                                                                                    - A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes.
                                                                                    Other developers report this intermittently as well.
                                                                                    - Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
                                                                                    agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
                                                                                    Requirements
                                                                                    Planned Changes
                                                                                    Litware plans to make the following changes:
                                                                                    - Ensure that agent1 can access all the tools in the environment.
                                                                                    - Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
                                                                                    - Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
                                                                                    - Ensure that Copilot retains details that it has learned and uses that knowledge for future work.
                                                                                    This must be applied to all licensed members of the organization.
                                                                                    Implementation guidelines
                                                                                    The development team at Litware identifies the following implementation guidelines:
                                                                                    - Agent workflows must be able to run in parallel.
                                                                                    - Application error handling must use the repository ErrorHandler class.
                                                                                    - agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
                                                                                    Security requirements
                                                                                    Litware identifies the following security requirements:
                                                                                    - Only the members of SG_Review must be able to approve agent1 plan outputs.
                                                                                    - All API keys must be stored and accessed securely.
                                                                                    - The developers must NOT be able to self-approve.
                                                                                    Agent configuration

                                                                                    You need to configure agent1 to support the planned changes.
                                                                                    What should you do?

                                                                                    A) Add the mcp-servers property to the agent configuration.
                                                                                    B) In the agent configuration, replace line 05 with the following.05 tools: [].
                                                                                    C) Delete line 05 from the agent configuration.
                                                                                    D) Add Use all available tools to the .github/copilot-instructions.md file.
                                                                                    E) Add Use all available tools to the instructions in the agent configuration.


                                                                                    Solutions:

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

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