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Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Implement tool use and environment interaction | 20–25% | - MCP server configuration
- 1. Add MCP servers to agents
- 2. Configure registries and allow lists
- Safe execution and error handling
- 1. Escalation paths and traceability
- 2. Retries and rollback strategies
- Development environment integration
- 1. Enable CI-based agent execution
- 2. Enable autonomous actions (PRs, branches)
- 3. Scope agents to repositories or branches
- Agent tool configuration
- 1. Select and configure tools
- 2. Configure tool permissions and scope
|
| Topic 2: Prepare agent architecture and SDLC processes | 15–20% | - Planning vs execution boundaries
- 1. Separate planning and execution phases
- 2. Prevent execution before approval
- 3. Validate structured agent plans
- 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. Define inputs, outputs, and success criteria
- 2. Define agent steps in SDLC
- 3. Identify and mitigate agent anti-patterns
|
| Topic 3: Manage memory, state, and execution | 10–15% | - 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
- State persistence and drift control
- 1. Detect and correct context drift
- 2. Persist task progress as artifacts
|
| Topic 4: Evaluation, error analysis, and tuning | 15–20% | - Define evaluation criteria
- 1. Generate automated evaluation signals
- 2. Define success metrics and constraints
- Tuning agent behavior
- 1. Refine prompts, tools, and workflows
- 2. Optimize memory usage and constraints
- Failure analysis
- 1. Classify reasoning, tool, and context errors
- 2. Analyze logs, traces, and artifacts
|
| Topic 5: Orchestrate multi-agent coordination | 15–20% | - Failure handling and recovery
- 1. Implement rollback and recovery patterns
- 2. Detect stalled or degraded agents
- Multi-agent workflows
- 1. Coordinate parallel agent execution
- 2. Resolve conflicts and overlaps
- Lifecycle management
- 1. Add/replace/retire agents safely
- Observability and auditability
- 1. Generate logs and artifacts for review
- 2. Document agent handoffs and decisions
|
| Topic 6: 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
|
Microsoft GitHub Agentic AI Developer Sample Questions:
1. 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) /compact
B) /context
C) /diff
D) /plan
2. During a Copilot CLI session, an MCP tool call fails because the external service requires re- authentication. What is the most likely resolution path?
A) Run /diff to inspect changes
B) Run /compact to clear context
C) Switch to plan mode
D) Re-authenticate the MCP server connector
3. After several long agentic sessions, you notice degraded response quality and want the agent to retain a concise summary of prior progress rather than the full raw history, while continuing without interruption. Which approach addresses this?
A) /compact
B) /clear
C) .copilotignore
D) --allow-all
4. 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) CODEOWNERS
B) .copilotignore
C) MCP server integration
D) Tool/terminal invocation permissions in agent mode
5. Drag and Drop Question
You have a GitHub repository that runs an agentic software development lifecycle (SDLC) workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel, and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors, and then fan in the results to a final executor.
How should you complete the workflow definition? 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.

Solutions:
Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: Only visible for members |