- Exam Code: GH-600
- Exam Name: GitHub Agentic AI Developer
- Updated: Sep 07, 2026
- Q & A: 85 Questions and Answers
Microsoft GitHub Agentic AI Developer : GH-600
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Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Manage memory, state, and execution | 10–15% | - Agent memory strategies
|
| Topic 2: Evaluation, error analysis, and tuning | 15–20% | - Define evaluation criteria
|
| Topic 3: Implement guardrails and accountability | 10–15% | - Autonomy and risk levels
|
| Topic 4: Orchestrate multi-agent coordination | 15–20% | - Observability and auditability
|
| Topic 5: Prepare agent architecture and SDLC processes | 15–20% | - Observability and control
|
| Topic 6: Implement tool use and environment interaction | 20–25% | - Agent tool configuration
|
Microsoft GitHub Agentic AI Developer Sample Questions:
Question #1
Drag and Drop Question
You have a GitHub Enterprise Cloud Organization that uses the GitHub Copilot coding agent to resolve issues asynchronously.
When an issue is assigned to GitHub Copilot, the agent creates a draft pull request, but your team cannot always tell whether the agent is actively working, has completed its session, or is awaiting workflow approval.
Which execution context does each signal indicate? To answer, drag the appropriate context to the correct signals. Each signal 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.
Answer:
Explanation:
Box 1: The agent acknowledges the assignment and will create the draft pull request.
When an issue is assigned to the GitHub Copilot coding agent, the eyes emoji reaction indicates that the agent has acknowledged the task and is actively starting work in the background.
Box 2: The agent session is actively running and generating live logs.
The signal indicating that "the pull request timeline shows Copilot started work" means that the agent session is actively running and generating live logs.
When a pull request timeline shows that Copilot started work, it indicates that the execution context is actively working.
Actively working: Indicated when the pull request timeline explicitly logs that Copilot started work or updates the PR body with a list of in-progress sub-tasks.
Box 3: A human must manually approve and run the workflow.
When a draft pull request exists but GitHub Actions checks are not running, it indicates that the execution context is awaiting workflow approval.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/kick-off-a-task
Question #2
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. operation:remove
- B. action:workflows.run
- C. action:artifact.destroy
- D. repo:<org>/<repo>
Answer: C
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Question #3
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.
Answer:
Explanation:
Box 1: inputs.ref
The specific commit being targeted.
Box 2: agents/rollout-bot.agent.md
The profile file to validate
Box 3: ${{ github.sha }}
The exact commit ref that the checkout action fetched
Reference:
https://octopus.com/devops/github-actions/github-actions-workflows/
Question #4
You are debugging an agentic workflow that intermittently fails specific tool calls with rate-limit errors when connecting to an internal MCP server. What is the most direct remediation?
- A. Switch to --allow-all
- B. Increase the MCP server's configured rate limits/quota
- C. Run /clear
- D. Add a CODEOWNERS entry
Answer: B
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Question #5
While using agent mode in VS Code, you want Copilot to run a specific test suite as a validation step after making changes, without manually invoking the terminal each time. What feature enables this?
- A. .copilotignore
- B. CODEOWNERS
- C. Tool/terminal invocation permissions in agent mode
- D. MCP server integration
Answer: C
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