GitHub Agentic AI Developer: GH-600 Exam
"GitHub Agentic AI Developer", also known as GH-600 exam, is a Microsoft Certification. With the complete collection of questions and answers, Pass4sureCert has assembled to take you through 85 Q&As to your GH-600 Exam preparation. In the GH-600 exam resources, you will cover every field and category in GitHub Administrator Certification helping to ready you for your successful Microsoft Certification.
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- Updated on: Aug 08, 2026
- No. of Questions: 85 Questions & Answers
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- Updated on: Aug 08, 2026
- No. of Questions: 85 Questions & Answers
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Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement tool use and environment interaction | 20–25% | - Development environment integration
|
| Implement guardrails and accountability | 10–15% | - Autonomy and risk levels
|
| Manage memory, state, and execution | 10–15% | - State persistence and drift control
|
| Prepare agent architecture and SDLC processes | 15–20% | - Planning vs execution boundaries
|
| Evaluation, error analysis, and tuning | 15–20% | - Tuning agent behavior
|
| Orchestrate multi-agent coordination | 15–20% | - Failure handling and recovery
|
Microsoft GitHub Agentic AI Developer Sample Questions:
1. Hotspot Question
You have a GitHub repository that uses GitHub Actions to validate pull requests opened by the GitHub Copilot coding agent. The workflow runs unit tests and a linter on pull request triggers, and Copilot opens draft pull requests on dedicated branches while iterating by using commits.
You discover that when multiple Copilot sessions push updates to the same pull request branch in quick succession, multiple workflow runs execute concurrently.
You need to enable parallel workflow executions across different pull request branches.
How should you configure workflow-level concurrency? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
2. Hotspot Question
You have a GitHub repository that uses GitHub Copilot Chat in Microsoft Visual Studio Code.
Custom agents are stored in the repository under version control.
Your team uses a multi-agent workflow where a planner agent produces an implementation plan that is then handed off to an implementation agent to make changes.
Recent prompts cause the planner agent to start editing files and running commands before the plan is approved.
You need to configure the planner agent to meet the following requirements:
- Use only read-only tools.
- Hand off to the implementation agent only after the plan is approved.
How should you configure the agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
3. 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) .copilotignore
D) /usage
4. 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
You need to provide access to the API key of MCP1. The solution must meet the security requirements.
What should you do?
A) Store the API key as a GitHub Codespaces user secret scoped to product-api.
B) In the product-api repository settings, add the API key directly to the .mcp/server.json file by using a plaintext apiKey field.
C) In product-api, add the API key as a GitHub Actions encrypted secret and reference the secret by using ${{ secrets.KEY }} in the workflow YAML of agent1.
D) Store the API key as a secret in the Copilot environment of product-api by using a name prefix of COPILOT_MCP_, and then reference the variable name in the mcp.json configuration.
5. Your organization wants to ensure the Copilot coding agent cannot push directly to main, and all its changes must go through a pull request review process like any other contributor. What enforces this?
A) Standard branch protection rules on the main branch
B) copilot-instructions.md
C) .copilotignore
D) /context
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: Only visible for members | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: A |
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