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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Orchestrate multi-agent coordination	15–20%	- Monitor and troubleshoot multi-agent execution - Design workflows for multiple agents - Define communication and handoff protocols - Prevent conflicts and manage shared resources
Perform evaluation, error analysis, and tuning	15–20%	- Define metrics and quality standards for outputs - Diagnose failures, hallucinations, and unexpected behavior - Optimize prompts, tools, and behavior through iteration - Test, validate, and compare agent results
Implement tool use and environment interaction	20–25%	- Configure and extend GitHub Copilot agents - Connect agents to codebase, APIs, and external systems - Manage permissions and environment access - Implement tools, custom actions, and MCP servers
Implement guardrails and accountability	10–15%	- Add validation, review, and approval gates - Enforce least privilege and security boundaries - Log actions, decisions, and changes for audit - Ensure compliance, safety, and responsible use
Prepare agent architecture and SDLC processes	15–20%	- Integrate agents into software development lifecycle - Design agent autonomy and decision boundaries - Define agent purpose, scope, and success criteria - Plan agent deployment, monitoring, and maintenance
Manage memory, state, and execution	10–15%	- Handle execution flow, retries, and interruptions - Choose memory types: short-term, long-term, external - Scope and persist agent state correctly - Implement memory cleanup and expiration rules

Microsoft GitHub Agentic AI Developer Sample Questions:

1. You have a GitHub Copilot coding agent named Orchestrator that runs a multi-phase workflow by using the following subagents:
- Explorer gathers context by using read-only tools.
- Modifier applies focused edits.
You are adding a new agent named Summarizer that generates a concise summary after modifications are complete. Summarizer includes the following YAML frontmatter.

The Orchestrator agent lists all three agents in its agents property.
After adding the Summarizer agent, Orchestrator successfully runs Explorer and Modifier but fails to run Summarizer.
What is a possible cause of the failure?

A) Orchestrator cannot call Summarizer because user-invocable is set to false.
B) Summarizer is missing the editing tools required to complete the workflow.
C) Orchestrator is missing a handoff entry to trigger Summarizer.
D) Summarizer cannot be invoked as a subagent because disable-model-invocation is set to true.

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 resolve the scoping issue associated to agent1.
What should you do?

A) Create a fine-grained personal access token (PAT) scoped to product-api and store the PAT as a GitHub Actions secret for agent1 to use.
B) Create a ruleset for billing-service and infra-terraform that blocks push access from the github- actions bot account.
C) Add a permissions block to the agent1 workflow in product-api.
D) To the profile of agent1, add a custom instruction specifying that the agent must NOT access billing-service or infra-terraform.

3. You are architecting an agentic AI system and need the agent's tool-calling behavior to be constrained so it can only call a specific allow-listed set of MCP tools, never arbitrary ones. What should you configure?

A) /usage
B) Tool/server allow-list in the MCP client configuration
C) .copilotignore
D) Repository ruleset

4. You have multiple GitHub Copilot coding agents that run tasks concurrently.
You live stream the session log output and see the following.

What is a possible cause of the error?

A) a postToolUse hook
B) a Copilot firewall policy
C) a GitHub app installation policy
D) a preToolUse hook

5. 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.