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Microsoft GH-600 Exam Syllabus Topics:

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

Microsoft GitHub Agentic AI Developer Sample Questions:

1. You have a private GitHub repository that has Copilot memory enabled.
Several developers who have write access to the repository make changes across multiple branches, including creating some pull requests that are later closed without merging.
Your team needs to understand how GitHub Copilot ensures that only task-relevant, up-to-date information influences code suggestions, even when older memories exist.
How does Copilot manage memories?

A) Copilot stores memories per user, ensuring that only the developer who created a memory can trigger the memory in future sessions.
B) Copilot validates each memory's citations against the current branch before using the memory, and ignores the memory if the referenced code no longer exists.
C) Copilot automatically blocks memory creation from pull requests that are closed without merging, to prevent outdated information from being stored.
D) Copilot stores memories indefinitely until a repository administrator deletes them manually.

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 provide access to the API key of MCP1. The solution must meet the security requirements.
What should you do?

A) In product-api, add the API key as a GitHub Actions encrypted secret and reference the secret by using ${{ secrets.KEY }} in the workflow YAML of agent1.
B) Store the API key as a GitHub Codespaces user secret scoped to product-api.
C) Store the API key as a secret in the Copilot environment of product-api by using a name prefix of COPILOT_MCP_, and then reference the variable name in the mcp.json configuration.
D) In the product-api repository settings, add the API key directly to the .mcp/server.json file by using a plaintext apiKey field.

3. You want to prevent GitHub Copilot from ever suggesting completions or making edits inside a directory containing sensitive credentials templates. What should you configure?

A) A repository ruleset
B) A .copilotignore file
C) A CODEOWNERS file
D) Branch protection rules

4. Hotspot Question
Your company uses GitHub Copilot custom agents in Microsoft Visual Studio Code.
The company also uses the Copilot coding agent on GitHub issues.
You have a file named .planner.agent.md that defines an agent named planner.planner has tools set to ['search', 'read', 'fetch']. There are explicit instructions NOT to write or modify any code. The file also defines a handoff labeled Start Implementation to an agent named implementer and sets send to false.
Developers report that after the planner agent produces a plan, implementation sometimes starts immediately in the same conversation, and code changes appear without an explicit agent switch.
When Copilot-created pull requests stall, maintainers review the pull request timeline and session logs. Several stalled sessions show outbound network commands blocked by a firewall, and the repositories do NOT contain a .github/copilot-instructions.md file.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

5. You are debugging an agentic workflow that intermittently fails specific tool calls with rate-limit errors when connecting to an internal MCP server. What is the most direct remediation?

A) Switch to --allow-all
B) Increase the MCP server's configured rate limits/quota
C) Run /clear
D) Add a CODEOWNERS entry

Solutions:

Question # 1
Answer: B

Question # 2
Answer: C

Question # 3
Answer: B

Question # 4
Answer: Only visible for members

Question # 5
Answer: B