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Microsoft GH-600 Exam Syllabus Topics:

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

Microsoft GitHub Agentic AI Developer Sample Questions:

Question 1

You have a GitHub Enterprise repository that uses the GitHub Copilot coding agent and opens draft pull requests for assigned issues.
Evaluation results show that the agent repeatedly opens pull requests that modify .github/workflows/*.yml to bypass failing checks instead of fixing the underlying code.
You need to ensure that the agent fixes the underlying code instead of bypassing the failing checks.
What should you do?

A. Update .github/copilot-instructions.md.
B. Start the GitHub Copilot CLI by using --allow-all.
C. Create a branch ruleset.
D. Disable GitHub Actions workflows for pull requests created by the agent.

Question 2

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.
You need to make changes to repo1 to support the planned changes for the agents.
What should you modify?

A. .vscode/settings.json
B. .github/agents/*.agent.md
C. <project>/.mcp/server.json
D. .vscode/mcp.json

Question 3

You have a repository on github.com that uses the GitHub Copilot coding agent.
You also use the GitHub Copilot CLI locally to reproduce failures and continue the same work from your terminal.
You need to verify the current token usage.
Which Copilot CLI slash command should you run?

A. /diff
B. /context
C. /usage
D. /compact

Question 4

You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?

A. Increase MCP server rate limits
B. Switch the agent to --allow-all mode
C. Enable Copilot memory
D. Add clear, reproducible steps and expected behavior to the issue

Question 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. .copilotignore
B. /context
C. copilot-instructions.md
D. Standard branch protection rules on the main branch

Solutions:

Question 1
Answer: C

Question 2
Answer: D

Question 3
Answer: B

Question 4
Answer: D

Question 5
Answer: D