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Exam Code: GH-600
Exam Name: GitHub Agentic AI Developer
Updated: Aug 16, 2026
Q & A: 85 Questions and Answers
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| Section | Weight | Objectives |
|---|---|---|
| Implement tool use and environment interaction | 20–25% | - Configure and extend GitHub Copilot agents - Implement tools, custom actions, and MCP servers - Manage permissions and environment access - Connect agents to codebase, APIs, and external systems |
| Manage memory, state, and execution | 10–15% | - Scope and persist agent state correctly - Implement memory cleanup and expiration rules - Choose memory types: short-term, long-term, external - Handle execution flow, retries, and interruptions |
| Prepare agent architecture and SDLC processes | 15–20% | - Integrate agents into software development lifecycle - Define agent purpose, scope, and success criteria - Design agent autonomy and decision boundaries - Plan agent deployment, monitoring, and maintenance |
| Perform evaluation, error analysis, and tuning | 15–20% | - Test, validate, and compare agent results - Optimize prompts, tools, and behavior through iteration - Diagnose failures, hallucinations, and unexpected behavior - Define metrics and quality standards for outputs |
| Implement guardrails and accountability | 10–15% | - Enforce least privilege and security boundaries - Ensure compliance, safety, and responsible use - Log actions, decisions, and changes for audit - Add validation, review, and approval gates |
| Orchestrate multi-agent coordination | 15–20% | - Design workflows for multiple agents - Monitor and troubleshoot multi-agent execution - Define communication and handoff protocols - Prevent conflicts and manage shared resources |
1. You have a private GitHub repository that has Copilot memory enabled.
Several developers who have write access to the repository make changes across multiple branches, including creating some pull requests that are later closed without merging.
Your team needs to understand how GitHub Copilot ensures that only task-relevant, up-to-date information influences code suggestions, even when older memories exist.
How does Copilot manage memories?
A) Copilot stores memories indefinitely until a repository administrator deletes them manually.
B) Copilot validates each memory's citations against the current branch before using the memory, and ignores the memory if the referenced code no longer exists.
C) Copilot stores memories per user, ensuring that only the developer who created a memory can trigger the memory in future sessions.
D) Copilot automatically blocks memory creation from pull requests that are closed without merging, to prevent outdated information from being stored.
2. A team assigns an issue to the GitHub Copilot coding agent by using the following one-line description: Fix the login bug.
Copilot creates a pull request, but the pull request is missing changes and has an incorrect scope.
How should you resolve the issue?
A) Enable Copilot memory.
B) Allocate more resources to copilot-setup-steps.yml.
C) Update the Model Context Protocol (MCP) server rate limits.
D) Add a clear description of the problem to the issue.
3. Case Study 1 - Contoso, Ltd
Overview
Contoso Ltd. is a software development company located in the United States.
Existing Environment
GitHub Environment
Contoso uses GitHub Enterprise and assigns GitHub Copilot Pro+ licenses to its developers. The developers use Microsoft Visual Studio Code as their IDE.
Contoso has a customer portal. The code for the portal is stored in a GitHub repository named repo1that contains the following:
- A custom agent named agent1 that includes instructions to review specs related to best practices
- A custom instruction file named validate-instructions.md that is used to validate tone of voice and applies to all .md and .txt files
- A custom instruction file named codereview.instructions.md that is used by the Copilot coding agent but is excluded for use by the Copilot code review repo1 has the following structure:
- The front-end is stored in the /frontend folder.
- The API logic is stored in the /api folder.
Contoso has a second repository named repo2 that contains a legacy .NET application named App1 built by using .NET 6. repo2 has a multi-agent workflow for modernization tasks.
Contoso enables the Model Context Protocol (MCP) registry and allows the Microsoft Learn MCP Server. Every developer must configure their own connection to the Learn MCP Server.
Problem Statements
The developers working in repo1 report that the Microsoft Learn documentation is NOT being retrieved when they attempt to validate a design by using agent1.
The testing team at Contoso identifies that the customer portal uses inconsistent UI styles, which leads to customer confusion and branding issues. The UI inconsistencies stem from variations in the folder structure.
Agent Logs
You have the following logs for the multi-agent workflow used in repo2.
Requirements
Planned Changes
Contoso plans to have all agents and developers in repo1use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
Contoso plans to leverage AI-powered coding agents to implement new portal features and pages.
Technical Requirements
App1 must be upgraded to .NET 10. A previous upgrade attempt was started by using the Copilot modernization agent, but the attempt was never finalized.
You plan to retry the upgrade. You must first analyze App1 by using AI, and then generate a report that contains breaking changes and deprecated patterns before retrying the upgrade.
All AI-generated code for UI styling must adhere to a predefined folder structure.
The architects at Contoso need help building implementation plans for repo1. The company wants to implement a new agent named agent2 to analyze the code base and the code requirements, and then respond with a detailed plan. The agent must NOT be able to edit files or run local commands.
The developers must be able to delegate work to the Copilot coding agent by assigning issues to the agent.
Hotspot Question
You need to implement agent2 to meet the technical requirements.
How should you complete the YAML configuration? 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.
4. You want a reusable, shareable set of instructions that defines a specific persona/workflow (e.g.,
"security reviewer") that developers can invoke on demand in Copilot Chat, rather than instructions applied globally to every session. What should you create?
A) copilot-instructions.md
B) .copilotignore
C) agents.md
D) A prompt file (.prompt.md)
5. 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.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: Only visible for members | Question # 4 Answer: D | Question # 5 Answer: Only visible for members |
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