Microsoft GitHub Agentic AI Developer : GH-600

  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Aug 13, 2026
  • Q & A: 85 Questions and Answers

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About Microsoft GitHub Agentic AI Developer : GH-600 Exam

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Microsoft GH-600 Exam Syllabus Topics:

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

Microsoft GitHub Agentic AI Developer Sample Questions:

1. You have a GitHub Enterprise repository.
An agent opens pull requests to the main branch.
You need to ensure that changes to .github/workflows/* and /infra/* require approval from designated reviewers before merge.
What should you configure?

A) a ruleset and an agents.md file
B) a ruleset and a .copilotignore file
C) a branch protection rule and a CODEOWNERS file
D) a branch protection rule and copilot-instructions.md


2. Case Study 2
Existing Environment
GitHub Environment
The GitHub environment contains the following:
- Three repositories named product-api, billing-service, and infra-terraform.
- Branch protection on the main branch in all repositories that requires at least one pull request review before merging
- GitHub Actions runners used across all workflows
- A GitHub team named SG_Dev that contains developers
- A GitHub team named SG_Review that contains senior engineers and a security team
- A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
- No custom agent profile is defined.
- A Model Context Protocol (MCP) server named MCP1 is deployed to
https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
MCP1 requires an API key for authentication.
A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
Copilot memory is NOT enabled for the organization.
Problem Statements
Litware identifies the following issues:
- During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope.
- agent1 makes code changes immediately after receiving a task.
- A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes.
Other developers report this intermittently as well.
- Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
Requirements
Planned Changes
Litware plans to make the following changes:
- Ensure that agent1 can access all the tools in the environment.
- Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
- Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
- Ensure that Copilot retains details that it has learned and uses that knowledge for future work.
This must be applied to all licensed members of the organization.
Implementation guidelines
The development team at Litware identifies the following implementation guidelines:
- Agent workflows must be able to run in parallel.
- Application error handling must use the repository ErrorHandler class.
- agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
Security requirements
Litware identifies the following security requirements:
- Only the members of SG_Review must be able to approve agent1 plan outputs.
- All API keys must be stored and accessed securely.
- The developers must NOT be able to self-approve.
Agent configuration

Hotspot Question
You are evaluating how agent1 will behave after you implement the planned changes.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.


3. Case Study 2
Existing Environment
GitHub Environment
The GitHub environment contains the following:
- Three repositories named product-api, billing-service, and infra-terraform.
- Branch protection on the main branch in all repositories that requires at least one pull request review before merging
- GitHub Actions runners used across all workflows
- A GitHub team named SG_Dev that contains developers
- A GitHub team named SG_Review that contains senior engineers and a security team
- A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
- No custom agent profile is defined.
- A Model Context Protocol (MCP) server named MCP1 is deployed to
https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
MCP1 requires an API key for authentication.
A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
Copilot memory is NOT enabled for the organization.
Problem Statements
Litware identifies the following issues:
- During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope.
- agent1 makes code changes immediately after receiving a task.
- A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes.
Other developers report this intermittently as well.
- Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
Requirements
Planned Changes
Litware plans to make the following changes:
- Ensure that agent1 can access all the tools in the environment.
- Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
- Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
- Ensure that Copilot retains details that it has learned and uses that knowledge for future work.
This must be applied to all licensed members of the organization.
Implementation guidelines
The development team at Litware identifies the following implementation guidelines:
- Agent workflows must be able to run in parallel.
- Application error handling must use the repository ErrorHandler class.
- agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
Security requirements
Litware identifies the following security requirements:
- Only the members of SG_Review must be able to approve agent1 plan outputs.
- All API keys must be stored and accessed securely.
- The developers must NOT be able to self-approve.
Agent configuration

Drag and Drop Question
You need to implement the security requirements for agent1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.


4. You have a GitHub repository that uses the GitHub Copilot coding agent.
Your company restricts GitHub Actions secrets.
Developers need the Copilot coding agent to call an internal dependency-scanning API during its run. The API requires an access token.
You need to ensure that the Copilot coding agent can use the token during execution without accessing the repository's Actions secrets and variables. The solution must prevent exposing the token in plaintext.
What should you do?

A) Add the token as an Actions repository secret.
B) Store the token in the agent configuration file.
C) Store the token in a repository custom instructions file.
D) Add the token as a secret in the Copilot environment.


5. A designated top-level instructions file is used by some agentic tools to describe overall repository purpose, build/test commands, and conventions in a tool-agnostic way (usable across multiple AI coding agents, not just Copilot). What is this file commonly called?

A) copilot-instructions.md
B) copilot-setup-steps.yml
C) .copilotignore
D) agents.md


Solutions:

Question # 1
Answer: C
Question # 2
Answer: Only visible for members
Question # 3
Answer: Only visible for members
Question # 4
Answer: D
Question # 5
Answer: D

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