Microsoft GH-600 : Developing in Agentic AI Systems

  • Exam Code: GH-600
  • Exam Name: Developing in Agentic AI Systems
  • Updated: Oct 09, 2026
  • Q & A: 111 Questions and Answers

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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Orchestrate multi-agent coordination15-20%- Detect and respond to multi-agent failures and degraded behavior
  • 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop
    • 2. Identify failed, partial, or stalled agent executions
      • 3. Respond to degraded behavior or coordination across agents
        - Manage the lifecycle of agents within multi-agent workflows
        • 1. Update, reconfigure, or replace agents without disrupting active workflows
          • 2. Retire agents while preserving auditability and workflow continuity
            • 3. Add agents to existing multi-agent workflows
              - Operate and manage multi-agent workflows
              • 1. Apply an orchestration pattern to coordinate multiple agents
                • 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs
                  • 3. Configure agent isolation for parallel execution
                    - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
                    • 1. Configure multi-agent workflows to produce artifacts suitable for review and audit
                      • 2. Perform post-hoc analysis of multi-agent behavior
                        • 3. Document key decisions, handoffs, and outcomes across agents
                          Implement tool use and environment interaction20-25%- Select and configure agent tools
                          • 1. Configure agent tool permissions
                            • 2. Identify required tools
                              • 3. Configure agent tools
                                - Configure MCP servers
                                • 1. Configure MCP allow lists
                                  • 2. Configure the MCP registries
                                    • 3. Add an MCP server as a tool to an agent
                                      • 4. Configure a GitHub remote MCP server
                                        - Integrate agents within development environments
                                        • 1. Configure an agent to be invoked in a CI workflow
                                          • 2. Configure an agent's scope to a specific repository
                                            • 3. Enable an agent to perform autonomous actions, including creating branches and pull requests
                                              • 4. Evaluate the execution context for an agent
                                                • 5. Configure an agent to use branch-based scope
                                                  • 6. Configure an agent to handle environment-specific constraints
                                                    - Operate agents with safe execution paths and robust error handling
                                                    • 1. Implement retries
                                                      • 2. Implement escalation paths
                                                        • 3. Implement traceability and accountability for agent actions
                                                          • 4. Implement error handling
                                                            • 5. Implement rollbacks
                                                              Prepare agent architecture and SDLC processes15-20%- Define boundaries between planning, reasoning, and action
                                                              • 1. Prevent agent action until the agent checked and approved
                                                                • 2. Validate agent plans
                                                                  • 3. Configure agent planning to be distinct from agent execution
                                                                    • 4. Configure an agent to output a structured plan
                                                                      - Configure observability and control for autonomous agents
                                                                      • 1. Configure human intervention for autonomous agents without slowing delivery
                                                                        • 2. Configure agents to produce inspectable artifacts within standard development tooling
                                                                          • 3. Plan and implement the degree of agent autonomy, including guardrails
                                                                            - Integrate agents into the software development lifecycle (SDLC)
                                                                            • 1. Define inputs, outputs, and success criteria for agents
                                                                              • 2. Identify and mitigate common anti-patterns in agents
                                                                                • 3. Identify steps for agents to perform
                                                                                  Implement guardrails and accountability10-15%- Implement guardrails and human-in-the-loop workflows
                                                                                  • 1. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes
                                                                                    • 2. Identify the subset of actions that require human judgment
                                                                                      • 3. Scope permissions and execution contexts to enforce least-privilege access
                                                                                        • 4. Preserve execution velocity by minimizing approvals that do not materially reduce risk
                                                                                          • 5. Block actions that violate defined security, compliance, or Responsible AI policies
                                                                                            - Define autonomy levels
                                                                                            • 1. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards
                                                                                              • 2. Classify agent actions by operational, security, and compliance risk to right-size human interventions
                                                                                                Perform evaluation, error analysis, and tuning15-20%- Tune agent behavior based on evaluation results
                                                                                                • 1. Refine memory usage
                                                                                                  • 2. Revise instructions, workflows, or constraints
                                                                                                    • 3. Refine tool usage and tool access
                                                                                                      - Analyze agent failures and identify root causes
                                                                                                      • 1. Identify failures by using logs, plans, traces, outputs, and workflow artifacts
                                                                                                        • 2. Classify root causes, including reasoning errors, tool misuse, and context or environment issues
                                                                                                          - Define success criteria and evaluation signals for agent tasks
                                                                                                          • 1. Align evaluation criteria with development intent
                                                                                                            • 2. Identify qualitative and quantitative evaluation signals to evaluate agents
                                                                                                              • 3. Generate evaluation signals by using automated scanning tools
                                                                                                                • 4. Specify expected outcomes and operational constraints for agent tasks
                                                                                                                  Manage memory, state, and execution10-15%- Ensure continuity of agent memory and state across tools and environments
                                                                                                                  • 1. Share agent state
                                                                                                                    • 2. Prevent conflicting context
                                                                                                                      • 3. Prevent stale context
                                                                                                                        - Implement agent memory strategies
                                                                                                                        • 1. Define memory expiration, pruning, and reset rules
                                                                                                                          • 2. Choose between short-term, long-term, and external memory
                                                                                                                            • 3. Scope agent memory to task-relevant information
                                                                                                                              - Persist agent state and manage context drift
                                                                                                                              • 1. Capture task progress and decisions as durable artifacts
                                                                                                                                • 2. Detect and correct drift during extended agent execution
                                                                                                                                  • 3. Resume agent work without repeating steps or diverging from prior decisions

                                                                                                                                    Microsoft Developing in Agentic AI Systems Sample Questions:

                                                                                                                                    Question #1

                                                                                                                                    You need to troubleshoot the issue reported by Dev1.
                                                                                                                                    What should you review?

                                                                                                                                    • A. The agent session log in the Agents panel.
                                                                                                                                    • B. The GITHUB_TOKEN permissions block in the agent1 workflow.
                                                                                                                                    • C. The GitHub Actions runner log for the session job.
                                                                                                                                    • D. The GitHub Actions usage metrics of repo1.
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: A  🗳️

                                                                                                                                    Question #2

                                                                                                                                    Your team uses a remote GitHub Model Context Protocol (MCP) server for workflows in the software development life cycle (SDLC).
                                                                                                                                    You need to commit a workspace-scoped MCP configuration to ensure that GitHub Copilot can connect to the GitHub-hosted MCP endpoint and authenticate by using a GitHub personal access token (PAT).
                                                                                                                                    How should you complete the mcp.json configuration file? 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.

                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer:

                                                                                                                                    Question #3

                                                                                                                                    You have a GitHub repository that uses GitHub Actions for CI.
                                                                                                                                    Your team is piloting the GitHub Copilot coding agent to autonomously create branches and open pull requests. The repository follows trunk-based development that uses main as the default branch.
                                                                                                                                    You need to ensure that the agent meets the following requirements:
                                                                                                                                    Changes to main can occur only by using pull requests that have at least one approval.
                                                                                                                                    When a pull request is opened, a validation workflow runs.
                                                                                                                                    How should you configure the repository? To answer, select the appropriate options in the answer area.

                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer:

                                                                                                                                    Question #4

                                                                                                                                    You have a GitHub repository that uses the GitHub Copilot coding agent and GitHub Copilot Chat.
                                                                                                                                    You need to add a GitHub Actions workflow that meets the following requirements:
                                                                                                                                    Blocks merges unless agent changes are reviewed by a human.
                                                                                                                                    Fails if the pull request author is the Copilot coding agent.
                                                                                                                                    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.

                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer:

                                                                                                                                    Question #5

                                                                                                                                    You have a GitHub Enterprise Cloud organization that uses GitHub Actions for CI/CD.
                                                                                                                                    You plan to enable an agent to update and deploy workflows that access production environment secrets.
                                                                                                                                    You need to select an autonomy level for a compliance-sensitive workflow.
                                                                                                                                    Which autonomy level should you select?

                                                                                                                                    • A. Human-in-the-loop with required manual approvals for production
                                                                                                                                    • B. Read-only analysis with no ability to trigger workflows
                                                                                                                                    • C. Autonomous execution limited to non-production environments
                                                                                                                                    • D. Fully autonomous execution without approvals
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: A  🗳️

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