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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Orchestrate multi-agent coordination	15–20%	- Lifecycle management 1. Add/replace/retire agents safely - Failure handling and recovery 1. Detect stalled or degraded agents 2. Implement rollback and recovery patterns - Observability and auditability 1. Document agent handoffs and decisions 2. Generate logs and artifacts for review - Multi-agent workflows 1. Coordinate parallel agent execution 2. Resolve conflicts and overlaps
Topic 2: 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. Detect and correct context drift 2. Persist task progress as artifacts - Agent memory strategies 1. Memory scoping and expiration rules 2. Short-term vs long-term memory selection
Topic 3: Evaluation, error analysis, and tuning	15–20%	- 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 - Define evaluation criteria 1. Generate automated evaluation signals 2. Define success metrics and constraints
Topic 4: Prepare agent architecture and SDLC processes	15–20%	- 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 - Planning vs execution boundaries 1. Prevent execution before approval 2. Separate planning and execution phases 3. Validate structured agent plans - Observability and control 1. Produce inspectable artifacts in GitHub 2. Define autonomy levels and guardrails 3. Enable human-in-the-loop controls
Topic 5: 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 6: Implement tool use and environment interaction	20–25%	- Development environment integration 1. Enable CI-based agent execution 2. Enable autonomous actions (PRs, branches) 3. Scope agents to repositories or branches - MCP server configuration 1. Add MCP servers to agents 2. Configure registries and allow lists - Safe execution and error handling 1. Retries and rollback strategies 2. Escalation paths and traceability - Agent tool configuration 1. Configure tool permissions and scope 2. Select and configure tools

Microsoft GitHub Agentic AI Developer Sample Questions:

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

2. 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 configure agent1 to support the planned changes.
What should you do?

A) Add Use all available tools to the .github/copilot-instructions.md file.
B) Delete line 05 from the agent configuration.
C) In the agent configuration, replace line 05 with the following.05 tools: [].
D) Add the mcp-servers property to the agent configuration.
E) Add Use all available tools to the instructions in the agent configuration.

3. While using agent mode in VS Code, you want Copilot to run a specific test suite as a validation step after making changes, without manually invoking the terminal each time. What feature enables this?

A) Tool/terminal invocation permissions in agent mode
B) MCP server integration
C) CODEOWNERS
D) .copilotignore

4. 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.

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

Solutions:

Question # 1
Answer: A

Question # 2
Answer: B

Question # 3
Answer: A

Question # 4
Answer: Only visible for members

Question # 5
Answer: B