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Microsoft GH-600 Exam Syllabus Topics:

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

Microsoft GitHub Agentic AI Developer Sample Questions:

1. You have a GitHub repository.
Developers use the GitHub Copilot CLI and repository-scoped hooks under .github/hooks/*.json.
You need to allow the Copilot CLI to automatically run low-risk Bash commands. The solution must prevent the autonomous execution of high-risk commands, such as sudo, rm -rf /, and curl ... | bash.
What should you do?

A) Configure a userPromptSubmitted hook that logs prompts and exits nonzero.
B) Add .github/hooks/logs/ to .gitignore.
C) Configure a preToolUse hook that returns permissionDecision: "deny".
D) Configure a sessionStart hook that prints a policy banner.

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.
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.

3. Hotspot Question
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests.
You assign an issue to Copilot. Copilot creates a draft pull request. The pull request timeline shows Copilot started work, followed by status updates. The most recent status update is 55 minutes old.
You discover that the agent is no longer making progress.
You need to ensure that the work resumes without redoing the completed steps or changing the previously chosen approach.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

4. You assigned an issue to the Copilot coding agent, and it opened a pull request. You want to inspect exactly what code changes were made before merging. Which CLI slash command lets you view the change set directly in the terminal?

A) /diff
B) /plan
C) /compact
D) /context

5. An agent working through the Copilot coding agent in GitHub Actions needs additional build dependencies installed before it can run tests. Where should you define this setup?

A) agents.md
B) copilot-instructions.md
C) copilot-setup-steps.yml
D) .copilotignore

Solutions:

Question # 1
Answer: C

Question # 2
Answer: Only visible for members

Question # 3
Answer: Only visible for members

Question # 4
Answer: A

Question # 5
Answer: C