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Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement tool use and environment interaction | 20–25% | - Connect agents to codebase, APIs, and external systems - Manage permissions and environment access - Implement tools, custom actions, and MCP servers - Configure and extend GitHub Copilot agents |
| Perform evaluation, error analysis, and tuning | 15–20% | - Diagnose failures, hallucinations, and unexpected behavior - Define metrics and quality standards for outputs - Optimize prompts, tools, and behavior through iteration - Test, validate, and compare agent results |
| Implement guardrails and accountability | 10–15% | - Log actions, decisions, and changes for audit - Ensure compliance, safety, and responsible use - Enforce least privilege and security boundaries - Add validation, review, and approval gates |
| Prepare agent architecture and SDLC processes | 15–20% | - Define agent purpose, scope, and success criteria - Integrate agents into software development lifecycle - Design agent autonomy and decision boundaries - Plan agent deployment, monitoring, and maintenance |
| Manage memory, state, and execution | 10–15% | - Implement memory cleanup and expiration rules - Choose memory types: short-term, long-term, external - Scope and persist agent state correctly - Handle execution flow, retries, and interruptions |
| Orchestrate multi-agent coordination | 15–20% | - Prevent conflicts and manage shared resources - Design workflows for multiple agents - Monitor and troubleshoot multi-agent execution - Define communication and handoff protocols |
Microsoft GitHub Agentic AI Developer Sample Questions:
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests. The repository uses GitHub Actions for CI, and reviewers rely on pull request timelines and workflow artifacts to understand what the agent did.
During long-running agent tasks, the reviewers lose track of decisions and validation steps, which causes repeated questions and reworks when context drifts between iterations.
You need to persist task progress and decisions as durable artifacts and ensure that the reviewers can verify what the agent did during and after execution by using GitHub as the system of record.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action 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.
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent.
Engineers assign issues to Copilot, and Copilot creates draft pull requests. The engineers start tasks either by assigning issues on github.com or by using GitHub Copilot Chat in an IDE. Reviewers request updates by leaving pull request comments.
You discover that sometimes, Copilot uses outdated requirements after a reviewer posts an updated instruction in a pull request comment, and, in several cases, Copilot fails to resume work from the comment.
You need to ensure that iteration requests are applied to the correct pull request session and are processed consistently.
What should you do?
- A. Ensure that the agent has write access to the repository. Instruct the reviewer to mention @github in the task comments.
- B. Ensure that the agent has write access to the repository. Instruct the reviewer to unassign and reassign the original issue to Copilot.
- C. Start a new task from Copilot Chat in the IDE. Instruct the reviewer to mention @github in the task comments.
- D. Ensure that the reviewer has write access to the repository. Instruct the reviewer to mention @copilot in the pull request comments.
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You have a GitHub Copilot Enterprise subscription. Developers use Microsoft Visual Studio Code and GitHub Copilot.
The developers want to share a custom agent that can review code changes but cannot modify files.
You need to prevent the agent from modifying repository content and appearing in the agents dropdown.
How should you configure the YAML frontmatter for the agent? 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.
You need to configure agent1 to support the planned changes.
What should you do?
- A. Add the mcp-servers property to the agent configuration.
- B. Add Use all available tools to the .github/copilot-instructions.md file.
- C. In the agent configuration, replace line 05 with the following: tools: []
- D. Add Use all available tools to the instructions in the agent configuration.
- E. Delete line 05 from the agent configuration.
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You need to implement agent2 to meet the technical requirements.
How should you complete the YAML configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
name: implementation-planner
description: Creates detailed implementation plans and technical specifications in markdown format tools: [
<Dropdown 1>,
<Dropdown 2>,
'microsoftdocs/mcp/docs_search',
'microsoftdocs/mcp/docs_fetch'
]
The accompanying image includes empty dropdown controls and recreated practice alternatives.
Topic 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 repo1 that 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 repo1 use 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.
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