Microsoft GH-600 : GitHub Agentic AI Developer

  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Aug 02, 2026   Q&As: 85 Questions and Answers

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Microsoft GH-600 Exam Syllabus Topics:
SectionWeightObjectives
Topic 1: Manage memory, state, and execution10-15%- Manage context and memory
  • 1. Handle long-running execution
  • 2. Maintain agent state
- Control execution flow
  • 1. Monitor execution lifecycle
  • 2. Recover from interruptions and failures
Topic 2: Implement tool use and environment interaction20-25%- Manage execution environments
  • 1. Configure environments for agents
  • 2. Control access to resources and services
- Select and configure agent tools
  • 1. Identify required tools
  • 2. Configure tools and permissions
Topic 3: Prepare agent architecture and SDLC processes15-20%- Define boundaries between planning, reasoning, and action
  • 1. Separate planning from execution
  • 2. Configure structured plans
  • 3. Validate and approve plans before execution
- Configure observability and control
  • 1. Enable human intervention when required
  • 2. Generate inspectable artifacts
  • 3. Implement autonomy levels and guardrails
- Integrate agents into the software development lifecycle
  • 1. Identify steps for agents to perform
  • 2. Define inputs, outputs, and success criteria
  • 3. Identify and mitigate agent anti-patterns
Topic 4: Implement guardrails and accountability10-15%- Ensure accountability
  • 1. Track actions and decisions
  • 2. Establish human-in-the-loop processes
- Implement governance controls
  • 1. Apply security and compliance requirements
  • 2. Configure approval workflows
Topic 5: Orchestrate multi-agent coordination15-20%- Ensure safe collaboration
  • 1. Coordinate execution across agents
  • 2. Prevent conflicts and unsafe actions
- Coordinate multiple agents
  • 1. Design multi-agent workflows
  • 2. Manage communication and task delegation
Topic 6: Perform evaluation, error analysis, and tuning15-20%- Improve agent behavior
  • 1. Perform error analysis
  • 2. Tune prompts, tools, and configurations
- Evaluate agent performance
  • 1. Measure outcomes and quality
  • 2. Analyze scans, logs, and artifacts
Microsoft GitHub Agentic AI Developer Sample Questions:

1. You need Copilot's coding agent to interact with an external ticketing system (e.g., Jira) so it can pull issue details as part of its workflow. What should you configure?

A) An MCP server
B) A branch protection ruleset
C) A .copilotignore rule
D) A CODEOWNERS entry


2. You want the Copilot coding agent to scan a large codebase and propose a full, human- reviewable step-by-step plan before writing any code. What should you do first?

A) Configure an MCP server
B) Enable --allow-all
C) Switch to plan mode
D) Run /compact


3. You need finer control, selecting specific files and describing precise natural-language changes to apply, rather than letting the agent decide the full scope of changes. Which Copilot Chat mode should you use?

A) Edit mode
B) Agent mode
C) Plan mode
D) Ask mode


4. You are about to start a complex refactoring task in the GitHub Copilot CLI.
Before Copilot makes any changes, you need to review and agree on the approach.
What should you do first?

A) From the Copilot CLI, run the /compact command.
B) Start the Copilot CLI and specify the --allow-all parameter.
C) Start the Copilot CLI and specify the --agent=Task parameter.
D) From the Copilot CLI, switch to plan mode.


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


Solutions:

Question # 1
Answer: A
Question # 2
Answer: C
Question # 3
Answer: A
Question # 4
Answer: D
Question # 5
Answer: B

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