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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement guardrails and accountability	10–15%	- Define autonomy levels 1. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards 2. Classify agent actions by operational, security, and compliance risk to right-size human interventions - Implement guardrails and human-in-the-loop workflows 1. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 2. Block actions that violate defined security, compliance, or Responsible AI policies 3. Identify the subset of actions that require human judgment 4. Preserve execution velocity by minimizing approvals that do not materially reduce risk 5. Scope permissions and execution contexts to enforce least-privilege access
Manage memory, state, and execution	10–15%	- Implement agent memory strategies 1. Choose between short-term, long-term, and external memory 2. Define memory expiration, pruning, and reset rules 3. Scope agent memory to task-relevant information - Persist agent state and manage context drift 1. Resume agent work without repeating steps or diverging from prior decisions 2. Detect and correct drift during extended agent execution 3. Capture task progress and decisions as durable artifacts - Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Prevent stale context 3. Share agent state
Orchestrate multi-agent coordination	15–20%	- Manage the lifecycle of agents within multi-agent workflows 1. Update, reconfigure, or replace agents without disrupting active workflows 2. Retire agents while preserving auditability and workflow continuity 3. Add agents to existing multi-agent workflows - Detect and respond to multi-agent failures and degraded behavior 1. Identify failed, partial, or stalled agent executions 2. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 3. Respond to degraded behavior or coordination across agents - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Perform post-hoc analysis of multi-agent behavior 2. Document key decisions, handoffs, and outcomes across agents 3. Configure multi-agent workflows to produce artifacts suitable for review and audit - Operate and manage multi-agent workflows 1. Configure agent isolation for parallel execution 2. Apply an orchestration pattern to coordinate multiple agents 3. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs
Prepare agent architecture and SDLC processes	15–20%	- Configure observability and control for autonomous agents 1. Plan and implement the degree of agent autonomy, including guardrails 2. Configure human intervention for autonomous agents without slowing delivery 3. Configure agents to produce inspectable artifacts within standard development tooling - Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Define inputs, outputs, and success criteria for agents 3. Identify steps for agents to perform - Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Configure an agent to output a structured plan 3. Prevent agent action until the agent checks and approves 4. Configure agent planning to be distinct from agent execution
Perform evaluation, error analysis, and tuning	15–20%	- Analyze agent failures and identify root causes 1. Classify root causes, including reasoning errors, tool misuse, and context or environment issues 2. Identify failures by using logs, plans, traces, outputs, and workflow artifacts - Define success criteria and evaluation signals for agent tasks 1. Specify expected outcomes and operational constraints for agent tasks 2. Align evaluation criteria with development intent 3. Generate evaluation signals by using automated scanning tools 4. Identify qualitative and quantitative evaluation signals to evaluate agents - Tune agent behavior based on evaluation results 1. Refine memory usage 2. Revise instructions, workflows, or constraints 3. Refine tool usage and tool access
Implement tool use and environment interaction	20–25%	- Integrate agents within development environments 1. Configure an agent to handle environment-specific constraints 2. Enable an agent to perform autonomous actions, including creating branches and pull requests 3. Configure an agent's scope to a specific repository 4. Configure an agent to be invoked in a CI workflow 5. Configure an agent to use branch-based scope 6. Evaluate the execution context for an agent - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement rollbacks 3. Implement retries 4. Implement escalation paths 5. Implement traceability and accountability for agent actions - Configure MCP servers 1. Configure a GitHub remote MCP server 2. Configure MCP allow lists 3. Configure MCP registries 4. Add an MCP server as a tool to an agent - Select and configure agent tools 1. Identify required tools 2. Configure agent tool permissions 3. Configure agent tools

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Microsoft Developing in Agentic AI Systems Sample Questions (Q93-Q98):

NEW QUESTION # 93
You need to troubleshoot the issue reported by Dev1.
What should you review?

A. The GitHub Actions usage metrics of repo1.
B. The GitHub Actions runner log for the session job.
C. The GITHUB_TOKEN permissions block in the agent1 workflow.
D. The agent session log in the Agents panel.

Answer: D

NEW QUESTION # 94
In Microsoft Visual Studio Code, you are using GitHub Copilot Chat to generate documentation for a new feature.
Earlier in the day, you used Copilot Chat extensively for an unrelated refactoring task.
You discover that the Copilot responses for the new documentation task are influenced by the earlier conversation.
You need Copilot to focus only on the current task and avoid using prior conversational context. The solution must NOT affect other conversations.
What should you do?

A. Use @file to restrict the Copilot context to the currently open file.
B. Change from sidebar chat to inline chat.
C. Modify the Chat > Agent: Max Requests setting.
D. Clear the chat history of the current conversation.
E. Start a new Copilot Chat conversation.

Answer: E

Explanation:
Starting a new Copilot Chat conversation establishes a separate conversational context for the documentation task. The unrelated refactoring exchanges remain in their original conversation instead of continuing to influence the new thread. This directly addresses the requirement to separate tasks while preserving other conversations.
Conversation history contributes information beyond the latest prompt. Earlier instructions, assumptions, terminology, and design decisions can continue to affect subsequent responses within the same session. A fresh conversation provides a clear task boundary, allowing the developer to supply the feature requirements and relevant documentation context explicitly.
Changing from sidebar chat to inline chat changes the interaction surface; it does not provide the same explicit separation of conversational history. Referencing a file supplies relevant context but does not reliably remove earlier instructions. The maximum requests setting governs agent execution limits rather than conversation isolation. Clearing history is also unnecessary when the previous work can remain available in its own session.
A new conversation can still receive applicable repository instructions and deliberately supplied context; it is the previous conversation that is separated.
Study-guide topics: conversational state, context isolation, and task boundaries. Reference: VS Code-Manage agent sessions.

NEW QUESTION # 95
You have a GitHub repository that contains the following custom agent files:
A file named planner.agent.md that includes YAML frontmatter with a handoffs entry that has label: Start Implementation, agent: implementer, and prompt: Now implement the plan outlined above.
A file named implementer.agent.md that is in the same directory as planner.agent.md and includes YAML frontmatter that has name: IMPLEMENTER.
You add a third agent file named review.agent.md that includes name: code-review in the YAML frontmatter.
You make the following changes to planner.agent.md:
Update the existing handoff to include send: true and model: GPT-5.2 (copilot).
Add a second handoff that has label: Run Review, agent: code-review, and prompt: Review the code changes made in the previous step.

Answer:

Explanation:

NEW QUESTION # 96
Hotspot Question
You have the following agent logs.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Yes
Yes, the agent did respond with messages.
Markdown Message: The log ccreq:XXX.copilotmd | markdown message 0 returned: finish reason: [stop] indicates that a text-based markdown response was successfully generated and completed.
Language Model Output: The log ccreq:XXX.copilotmd shows the core language model (gpt-4o- mini) completed its request, delivering the main content of the message.
Box 2: Yes
Based on the log entries provided, yes, the agent can edit files in the repository.
Evidence from the LogsTargeted Component: The final three log entries explicitly target the component [panel/editAgent].
Model Selection: For these specific tasks, the system switches from standard conversational models (gpt-4o-mini) to a specialized code-generation model: gpt-5.3-codex.
Processing Time: These operations take significantly longer than standard chat generation, which is characteristic of complex code-parsing and file-modification tasks.
Box 3: Yes
Based on the log entries provided, yes, the agent analyzed files in the repository.
Action Type: The final three entries explicitly call the [panel/editAgent] capability.
Model Used: These actions utilized the gpt-5.3-codex model, which is specialized for codebases.
Operation Duration: The execution times were significantly longer.
Implied Task: Code-generation and editing agents require repository file analysis to perform edits.
Reference:
https://learn.microsoft.com/en-us/troubleshoot/power-platform/copilot-studio/authoring/error-codes

NEW QUESTION # 97
You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?

A. Increase MCP server rate limits
B. Enable Copilot memory
C. Switch the agent to --allow-all mode
D. Add clear, reproducible steps and expected behavior to the issue

Answer: D

Explanation:
Agents rely entirely on the context provided in the issue. Vague issues lead to incorrect fixes.
Providing reproduction steps, error logs, and expected behavior gives the agent the information it needs to correctly diagnose and resolve the root cause.

NEW QUESTION # 98
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