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Microsoft GH-600 Exam Syllabus Topics:

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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q70-Q75):

NEW QUESTION # 70
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent.
Engineers assign issues to Copilot, and Copilot creates draft pull requests. The engineers start tasks either by assigning issues on github.com or by using GitHub Copilot Chat in an IDE.
Reviewers request updates by leaving pull request comments.
You discover that sometimes, Copilot uses outdated requirements after a reviewer posts an updated instruction in a pull request comment, and, in several cases, Copilot fails to resume work from the comment.
You need to ensure that iteration requests are applied to the correct pull request session and are processed consistently.
What should you do?

A. Start a new task from Copilot Chat in the IDE. Instruct the reviewer to mention @github in the task comments.
B. Ensure that the reviewer has write access to the repository. Instruct the reviewer to mention
@copilot in the pull request comments.
C. Ensure that the agent has write access to the repository. Instruct the reviewer to unassign and reassign the original issue to Copilot.
D. Ensure that the agent has write access to the repository. Instruct the reviewer to mention @github in the task comments.

Answer: B

Explanation:
You should explicitly instruct the reviewer to mention @copilot in the pull request comments, and you must verify that they have write access to the repository.
Explicit Triggering: GitHub Copilot's asynchronous coding agent listens for active summons in existing pull request threads. Simply leaving a comment without an explicit @copilot mention may cause it to skip processing the update entirely, explaining why it fails to resume work.
Context Preservation: By directly mentioning @copilot within the specific pull request comment thread, the agent binds the new instructions directly to that exact active development session.
This prevents it from pulling outdated requirements from the initial issue definition.
Permission Enforcement: GitHub Copilot will ignore commands and mentions from users who do not possess write permissions (or higher) on the repository to prevent unauthorized code modifications or resource usage.
Reference:
https://github.blog/ai-and-ml/github-copilot/assigning-and-completing-issues-with-coding-agent-in-github-copilot/

NEW QUESTION # 71
You have a GitHub Copilot Enterprise subscription. Developers use Microsoft Visual Studio Code and GitHub Copilot.
The developers want to share a custom agent that can review code changes but cannot modify files.
You need to prevent the agent from modifying repository content and appearing in the agents dropdown.
How should you configure the YAML frontmatter for the agent? 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.

Answer:

Explanation:

Explanation:
Target
Correct selection
tools
[ ' search ' , ' read ' ]
user-invocable
false
The agent needs inspection capabilities without file-modification capabilities. Setting tools to [ ' search ' , ' read ' ] gives it the ability to locate relevant code and examine file contents while excluding the editing capability supplied by edit. This is appropriate for a reviewer whose output consists of findings or recommendations rather than applied changes.
The alternative list containing edit would directly contradict the requirement. The agent tool enables delegation to another agent and is not the required substitute for searching and reading repository content.
Likewise, none is not the documented way to configure a useful read-only tool list.
Setting user-invocable: false independently prevents manual selection of the agent. That property governs discoverability and invocation, while tools governs available capabilities. Both settings are necessary because hiding an agent does not make its operations read-only, and restricting its tools does not automatically hide it.
The result is a specialized reviewer that can be incorporated into a controlled workflow without being offered as a normal user-selected editing agent.
Relevant curriculum topics are tool selection, least-privilege permissions, and agent invocation controls.
Reference: Custom agents configuration .

NEW QUESTION # 72
You have a GitHub repository that contains an agent named Orchestrator. Orchestrator delegates work to the following specialized subagents:
Planner reviews issues and creates a plan of action.
Implementer writes code based on the plan of action.
Reviewer reviews the code.
You create a new agent named Summarizer that produces a concise summary of the work performed by the other agents.
You need to ensure that Orchestrator can invoke Summarizer as part of its workflow.
What should you do?

A. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the tools list.
B. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
C. In the YAML frontmatter of the Reviewer agent, add a handoffs entry that points to the Summarizer agent.
D. In the YAML frontmatter of the Reviewer agent, add Summarizer to the agents list.

Answer: B

Explanation:
The configuration must be changed on the agent that performs the delegation. Orchestrator is responsible for coordinating the workflow, so its allowed subagent list must include Summarizer. Adding the new agent to that list makes it an available delegation target alongside Planner, Implementer, and Reviewer.
An agent's agents property identifies the custom agents it can invoke as subagents. The tools property serves a different purpose: it identifies available capabilities such as reading files, editing code, or invoking agents. Adding a custom agent's name to the tools list does not register that agent as a callable subagent. The agent invocation tool must also be available; the scenario already establishes that Orchestrator delegates to other subagents.
Changing Reviewer's configuration would enable a relationship originating from Reviewer, rather than the requested direct invocation by Orchestrator. A handoff also represents a different interaction pattern from having the orchestrating agent invoke a subagent and receive its result.
Availability does not force execution on every task. Orchestrator's instructions should indicate when a summary is required and what information it should contain.
Study-guide topics: delegation permissions, orchestrator responsibilities, and specialized subagents. Reference: VS Code-Subagents.

NEW QUESTION # 73
You need finer control, selecting specific files and describing precise natural-language changes to apply, rather than letting the agent decide the full scope of changes. Which Copilot Chat mode should you use?

A. Edit mode
B. Plan mode
C. Agent mode
D. Ask mode

Answer: A

Explanation:
Edit mode is designed for targeted, developer-directed changes: you select the files in scope and describe the specific modification, giving you more granular control than the fully autonomous agent mode.

NEW QUESTION # 74
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 statement, select Yes or No.

Answer:

Explanation:

NEW QUESTION # 75
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