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Microsoft GitHub Agentic AI Developer : GH-600
- Exam Code: GH-600
- Exam Name: GitHub Agentic AI Developer
- Updated: Sep 13, 2026 Q & A: 111 Questions and Answers
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About Microsoft GH-600 Exam
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Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Perform evaluation, error analysis, and tuning | 15–20% | - Test, validate, and compare agent results - Define metrics and quality standards for outputs - Diagnose failures, hallucinations, and unexpected behavior - Optimize prompts, tools, and behavior through iteration |
| Orchestrate multi-agent coordination | 15–20% | - Prevent conflicts and manage shared resources - Monitor and troubleshoot multi-agent execution - Design workflows for multiple agents - Define communication and handoff protocols |
| Manage memory, state, and execution | 10–15% | - Handle execution flow, retries, and interruptions - Scope and persist agent state correctly - Choose memory types: short-term, long-term, external - Implement memory cleanup and expiration rules |
| Prepare agent architecture and SDLC processes | 15–20% | - Define agent purpose, scope, and success criteria - Design agent autonomy and decision boundaries - Plan agent deployment, monitoring, and maintenance - Integrate agents into software development lifecycle |
| Implement guardrails and accountability | 10–15% | - Add validation, review, and approval gates - Ensure compliance, safety, and responsible use - Log actions, decisions, and changes for audit - Enforce least privilege and security boundaries |
| Implement tool use and environment interaction | 20–25% | - Configure and extend GitHub Copilot agents - Manage permissions and environment access - Connect agents to codebase, APIs, and external systems - Implement tools, custom actions, and MCP servers |
Microsoft GitHub Agentic AI Developer Sample Questions:
Question #1
You have a GitHub Actions workflow that runs a multi-agent job. Each agent uploads its output as a workflow artifact.
A completed workflow run produces unexpected code changes, and the job logs do NOT show the agents' intermediate outputs.
You need to retrieve the agents' captured outputs from the completed run for post-hoc analysis.
What should you do in GitHub Actions?
- A. Rerun the workflow with debug logging enabled.
- B. Review the repository's commit history for the run.
- C. Enable step-level masking for secrets in the workflow.
- D. Download the workflow artifacts from the run summary page.
Answer: D
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Question #2
You have a GitHub repository that has a GitHub Actions workflow. The workflow runs an AI agent that deploys a containerized service.
You need to update the workflow so that only one production deployment can run at a time, and any currently running production deployments are cancelled when a new run starts.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.
Answer:
Question #3
Your company uses GitHub Copilot custom agents in Microsoft Visual Studio Code.
The company also uses the Copilot coding agent on GitHub issues.
You have a file named .planner.agent.md that defines an agent named planner. planner has tools set to ['search', 'read', 'fetch']. There are explicit instructions NOT to write or modify any code. The file also defines a handoff labeled Start Implementation to an agent named implementer and sets send to false.
Developers report that after the planner agent produces a plan, implementation sometimes starts immediately in the same conversation and code changes appear without an explicit agent switch.
When Copilot-created pull requests stall, maintainers review the pull request timeline and session logs. Several stalled sessions show outbound network commands blocked by a firewall, and the repositories do NOT contain a .github/copilot-instructions.md file.
For each statement, select Yes or No.
Answer:
Question #4
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 pull requests are merged
- C. when the code that created the memory is deleted
- D. when the repository is archived
- E. automatically after 28 days
Answer: A,E
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Question #5
You are architecting an agentic AI system and need the agent's tool-calling behavior to be constrained so it can only call a specific allow-listed set of MCP tools, never arbitrary ones. What should you configure?
- A. Repository ruleset
- B. Tool/server allow-list in the MCP client configuration
- C. /usage
- D. .copilotignore
Answer: B
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