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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Orchestrate multi-agent coordination	15–20%	- Failure handling and recovery 1. Detect stalled or degraded agents 2. Implement rollback and recovery patterns - Lifecycle management 1. Add/replace/retire agents safely - Multi-agent workflows 1. Resolve conflicts and overlaps 2. Coordinate parallel agent execution - Observability and auditability 1. Generate logs and artifacts for review 2. Document agent handoffs and decisions
Topic 2: Implement guardrails and accountability	10–15%	- Autonomy and risk levels 1. Assign autonomy levels with compliance constraints 2. Classify agent actions by risk - Guardrails and human-in-the-loop 1. Enforce least-privilege execution 2. Require approvals for sensitive actions
Topic 3: Manage memory, state, and execution	10–15%	- Cross-tool continuity 1. Share state across tools and environments 2. Prevent stale or conflicting context - State persistence and drift control 1. Persist task progress as artifacts 2. Detect and correct context drift - Agent memory strategies 1. Short-term vs long-term memory selection 2. Memory scoping and expiration rules
Topic 4: Prepare agent architecture and SDLC processes	15–20%	- Observability and control 1. Enable human-in-the-loop controls 2. Produce inspectable artifacts in GitHub 3. Define autonomy levels and guardrails - Planning vs execution boundaries 1. Validate structured agent plans 2. Prevent execution before approval 3. Separate planning and execution phases - Integrate agents into SDLC workflows 1. Define agent steps in SDLC 2. Identify and mitigate agent anti-patterns 3. Define inputs, outputs, and success criteria
Topic 5: Implement tool use and environment interaction	20–25%	- Development environment integration 1. Enable autonomous actions (PRs, branches) 2. Enable CI-based agent execution 3. Scope agents to repositories or branches - Agent tool configuration 1. Configure tool permissions and scope 2. Select and configure tools - MCP server configuration 1. Configure registries and allow lists 2. Add MCP servers to agents - Safe execution and error handling 1. Escalation paths and traceability 2. Retries and rollback strategies
Topic 6: Evaluation, error analysis, and tuning	15–20%	- Define evaluation criteria 1. Generate automated evaluation signals 2. Define success metrics and constraints - Tuning agent behavior 1. Optimize memory usage and constraints 2. Refine prompts, tools, and workflows - Failure analysis 1. Classify reasoning, tool, and context errors 2. Analyze logs, traces, and artifacts

Microsoft GitHub Agentic AI Developer Sample Questions:

1. Drag and Drop Question
You have a GitHub repository that has a GitHub Actions workflow. The workflow runs an AI agent.
You need to ensure that the default GITHUB_TOKEN permissions are read-only, and write access is granted to only the job that performs repository write operations. The workflow must be able to create and approve pull requests only when explicitly enabled.
How should you complete the workflow? 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.

2. Drag and Drop Question
Your team uses a remote GitHub Model Context Protocol (MCP) server for workflows in the software development life cycle (SDLC).
You need to commit a workspace-scoped MCP configuration to ensure that GitHub Copilot can connect to the GitHub-hosted MCP endpoint and authenticate by using a GitHub personal access token (PAT).
How should you complete the mcp.json configuration file? 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.

3. You have a GitHub repository that uses GitHub Copilot code review on pull requests.
You plan to add repository-wide code review guidance that will apply to all files.
You need Copilot code review to consistently apply the guidance during pull request reviews.
What should you do?

A) Update the instructions in the .github/agents/*.agent.md files.
B) Create a .github/copilot-instructions.md file.
C) Modify the .github/pull_request_template.md file.
D) Create a .github/instructions/copilot.instructions.md file.

4. You have a GitHub Copilot coding agent named CodeAgent. The .agent.md file of CodeAgent contains the following YAML frontmatter.
---
name: CodeAgent
description: Performs repository analysis and code review tasks.
tools: ['edit', 'execute', 'read', 'search']
---
You need to issue a GitHub Copilot CLI command that preserves execution velocity for read-only tasks by eliminating approval prompts for low-risk tools. The solution must ensure that high-risk tools that can make changes remain available but still require explicit user approval before running.
Which command should you run?

A) copilot agent run CodeAgent --allow-tool 'read,search'
B) copilot agent run CodeAgent --deny-tool 'edit,execute'
C) copilot agent run CodeAgent
D) copilot agent run CodeAgent --allow-all-tools
E) copilot agent run CodeAgent --allow-tool 'read,search' --deny-tool 'edit,execute'

5. 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.
While upgrading App1, the agent identifies 47 issues, including a security vulnerability, and 46 API incompatibilities across different projects.
Which two actions are unsafe to delegate to the agent and require human involvement? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A) Approve all Git commits.
B) Generate the assessment.md file.
C) Review the plan.md file for dependencies.
D) Validate the assessment.md file for accuracy.
E) Validate whether the tasks.md file exists.

Solutions:

Question # 1
Answer: Only visible for members

Question # 2
Answer: Only visible for members

Question # 3
Answer: D

Question # 4
Answer: E

Question # 5
Answer: A,D