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Microsoft GH-600 Exam Syllabus Topics:

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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q34-Q39):

NEW QUESTION # 34
An enterprise administrator wants to audit which repositories have had Copilot coding agent- created pull requests over the last 30 days. Where should the administrator look?

A. copilot-setup-steps.yml
B. MCP server logs
C. Organization audit log
D. .copilotignore file

Answer: C

Explanation:
GitHub's organization/enterprise audit log records agent activity, including pull requests created by the Copilot coding agent, giving administrators visibility for compliance and governance purposes.

NEW QUESTION # 35
You have a GitHub repository that uses GitHub Actions for CI.
Your team is piloting the GitHub Copilot coding agent to autonomously create branches and open pull requests. The repository follows trunk-based development that uses main as the default branch.
You need to ensure that the agent meets the following requirements:
Changes to main can occur only by using pull requests that have at least one approval.
When a pull request is opened, a validation workflow runs.
How should you configure the repository? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

NEW QUESTION # 36
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 # 37
You have a GitHub Enterprise Cloud repository that uses GitHub Actions for CI and requires pull requests for all changes.
You are planning a GitHub Actions workflow where a coding agent drafts implementation changes and tests from GitHub issues, and an automated review runs before human review.
You need the agent to draft changes from an assigned issue, open a pull request, and add an automated review to the pull request before requesting a human review.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 38
You need to enable Copilot memory to support the planned changes.
What should you configure?

A. The personal Copilot settings of each developer
B. The Copilot settings of each repository
C. The Copilot settings of the organization
D. The agent profile of agent1

Answer: C

Explanation:
Copilot memory is an organization-level capability. Configuring it through the organization's Copilot settings establishes the governance boundary, availability, and policy controls consistently for the developers and repositories covered by that organization.
Personal settings are unsuitable because they create inconsistent behavior between developers and do not provide centralized administration. A repository-level configuration can supply repository instructions and scoped context, but it does not replace the organization-level control that enables and governs the memory capability. An individual agent profile defines role behavior, tool access, prompts, and delegation settings; it is not the tenant-level location for enabling Copilot memory.
Organization configuration is particularly important when agents must retain approved context, recurring pReference, and workflow guidance across sessions without each developer configuring behavior independently. Central governance also allows administrators to maintain predictable controls over which organizational information is available to Copilot.
Study-guide topics: organizational governance, persistent context, and centralized agent configuration.

NEW QUESTION # 39
......

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