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Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Perform evaluation, error analysis, and tuning | 15-20% | - Evaluate agent performance
- 1. Analyze scans, logs, and artifacts
- 2. Measure outcomes and quality
- Improve agent behavior
- 1. Tune prompts, tools, and configurations
- 2. Perform error analysis
|
| Implement guardrails and accountability | 10-15% | - Implement governance controls
- 1. Apply security and compliance requirements
- 2. Configure approval workflows
- Ensure accountability
- 1. Track actions and decisions
- 2. Establish human-in-the-loop processes
|
| Prepare agent architecture and SDLC processes | 15-20% | - Configure observability and control
- 1. Implement autonomy levels and guardrails
- 2. Generate inspectable artifacts
- 3. Enable human intervention when required
- Define boundaries between planning, reasoning, and action
- 1. Separate planning from execution
- 2. Configure structured plans
- 3. Validate and approve plans before execution
- Integrate agents into the software development lifecycle
- 1. Define inputs, outputs, and success criteria
- 2. Identify steps for agents to perform
- 3. Identify and mitigate agent anti-patterns
|
| Implement tool use and environment interaction | 20-25% | - Manage execution environments
- 1. Control access to resources and services
- 2. Configure environments for agents
- Select and configure agent tools
- 1. Configure tools and permissions
- 2. Identify required tools
|
| Manage memory, state, and execution | 10-15% | - Manage context and memory
- 1. Maintain agent state
- 2. Handle long-running execution
- Control execution flow
- 1. Recover from interruptions and failures
- 2. Monitor execution lifecycle
|
| Orchestrate multi-agent coordination | 15-20% | - Ensure safe collaboration
- 1. Coordinate execution across agents
- 2. Prevent conflicts and unsafe actions
- Coordinate multiple agents
- 1. Design multi-agent workflows
- 2. Manage communication and task delegation
|
Microsoft GitHub Agentic AI Developer Sample Questions:
1. You are running the GitHub Copilot CLI and want the agent to execute file edits and shell commands without prompting for confirmation on each individual action, during a trusted throwaway sandbox session. Which flag/command should you use?
A) /compact
B) --allow-all
C) --agent=Task
D) /context
2. Your organization requires that any workflow file changes proposed by an autonomous agent be reviewed by a member of the security team before merging, regardless of who opened the pull request. What combination should you configure?
A) copilot-instructions.md + required status checks
B) MCP server permissions + repository ruleset
C) CODEOWNERS + a required branch protection rule
D) .copilotignore + agents.md
3. Your team wants Copilot's suggestions to reflect knowledge of internal library APIs that are not publicly documented and not present in the codebase being edited. What is the most appropriate solution?
A) Enable plan mode
B) Add the internal docs as copilot-instructions.md
C) Connect an MCP server that exposes the internal documentation
D) Use .copilotignore to hide public APIs
4. 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) Summarizer cannot be invoked as a subagent because disable-model-invocation is set to true.
B) Orchestrator cannot call Summarizer because user-invocable is set to false.
C) Orchestrator is missing a handoff entry to trigger Summarizer.
D) Summarizer is missing the editing tools required to complete the workflow.
5. You are about to start a complex refactoring task in the GitHub Copilot CLI.
Before Copilot makes any changes, you need to review and agree on the approach.
What should you do first?
A) Start the Copilot CLI and specify the --agent=Task parameter.
B) From the Copilot CLI, switch to plan mode.
C) Start the Copilot CLI and specify the --allow-all parameter.
D) From the Copilot CLI, run the /compact command.
Solutions:
Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: B |