Microsoft GH-600 Valid Dump : Developing in Agentic AI Systems

GH-600 real exams

Exam Code: GH-600

Exam Name: Developing in Agentic AI Systems

Updated: Oct 04, 2026

Q & A: 111 Questions and Answers

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Microsoft GH-600 Exam Syllabus Topics:

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

                                                                                                                                    Microsoft Developing in Agentic AI Systems Sample Questions:

                                                                                                                                    Question #1

                                                                                                                                    You have a private GitHub repository that has Copilot memory enabled.
                                                                                                                                    Several developers who have write access to the repository make changes across multiple branches, including creating some pull requests that are later closed without merging.
                                                                                                                                    Your team needs to understand how GitHub Copilot ensures that only task-relevant, up-to-date information influences code suggestions, even when older memories exist.
                                                                                                                                    How does Copilot manage memories?

                                                                                                                                    • A. Copilot validates each memory's citations against the current branch before using the memory, and ignores the memory if the referenced code no longer exists.
                                                                                                                                    • B. Copilot stores memories indefinitely until a repository administrator deletes them manually.
                                                                                                                                    • C. Copilot automatically blocks memory creation from pull requests that are closed without merging, to prevent outdated information from being stored.
                                                                                                                                    • D. Copilot stores memories per user, ensuring that only the developer who created a memory can trigger the memory in future sessions.
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: A  πŸ—³οΈ

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                                                                                                                                    Question #2

                                                                                                                                    You have a GitHub Enterprise organization that has Copilot memory enabled.
                                                                                                                                    You create a new repository.
                                                                                                                                    What are two ways that memories will be deleted from the repository? Each correct answer presents a complete solution.
                                                                                                                                    NOTE: Each correct selection is worth one point.

                                                                                                                                    • A. manually by repository members
                                                                                                                                    • B. when the repository is archived
                                                                                                                                    • C. when the code that created the memory is deleted
                                                                                                                                    • D. when pull requests are merged
                                                                                                                                    • E. automatically after 28 days
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: A,E  πŸ—³οΈ

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                                                                                                                                    Question #3

                                                                                                                                    In Microsoft Visual Studio Code, you use GitHub Copilot agent mode for a repository. The GitHub Model Context Protocol (MCP) server is enabled.
                                                                                                                                    Your team maintains an internal orchestrator script that creates independent Copilot SDK sessions to process operational events from webhooks.
                                                                                                                                    You discover that the agent sometimes mixes context between unrelated webhook deliveries. Additionally, in some cases, the agent starts running tools before a human reviews the proposed steps.
                                                                                                                                    You need to ensure that the script meets the following business requirements:
                                                                                                                                    Planning must be kept separate from executing.
                                                                                                                                    Each plan must be captured without calling any tools.
                                                                                                                                    Each webhook event must create a new independent session.
                                                                                                                                    Once the plan is complete, tool calls must be enabled within the same event-specific session for follow-up.

                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer:

                                                                                                                                    Question #4

                                                                                                                                    You have a GitHub repository that uses the GitHub Copilot coding agent.
                                                                                                                                    The agent is assigned to a long-running issue and has already opened a draft pull request linked to the issue. The pull request timeline shows Copilot started work and the pull request description shows periodic status updates.
                                                                                                                                    After 70 minutes, the pull request stops receiving new commits, and the agent session log indicates that the session has timed out. In a pull request comment thread, the agent then proposes changes that no longer match the latest repository guidance.
                                                                                                                                    You need to resume execution in a way that reestablishes the correct context and produces new commits in the existing draft pull request.
                                                                                                                                    What should you do?

                                                                                                                                    • A. Create a new issue and assign the issue to Copilot.
                                                                                                                                    • B. Select Approve and run workflows in the pull request merge box.
                                                                                                                                    • C. Post a pull request comment that mentions @copilot.
                                                                                                                                    • D. Close and reopen the issue.
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: C  πŸ—³οΈ

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                                                                                                                                    Question #5

                                                                                                                                    You have a GitHub repository that uses a GitHub Actions workflow to run an agent-driven change plan as part of a CI pipeline. The workflow generates an artifact named plan.json that includes a field named risk. risk has possible values of low, medium, or high.
                                                                                                                                    You need to ensure that a human must confirm the execution of the workflow when risk is medium or high. The workflow must proceed automatically only when risk is low.
                                                                                                                                    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:

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