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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Perform evaluation, error analysis, and tuning	15–20%	- Define success criteria and evaluation signals for agent tasks 1. Generate evaluation signals by using automated scanning tools 2. Align evaluation criteria with development intent 3. Identify qualitative and quantitative evaluation signals to evaluate agents 4. Specify expected outcomes and operational constraints for agent tasks - 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 - Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Revise instructions, workflows, or constraints 3. Refine memory usage
Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Identify the subset of actions that require human judgment 2. Preserve execution velocity by minimizing approvals that do not materially reduce risk 3. Block actions that violate defined security, compliance, or Responsible AI policies 4. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 5. Scope permissions and execution contexts to enforce least-privilege access - 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 tool use and environment interaction	20–25%	- Operate agents with safe execution paths and robust error handling 1. Implement escalation paths 2. Implement retries 3. Implement traceability and accountability for agent actions 4. Implement error handling 5. Implement rollbacks - Select and configure agent tools 1. Configure agent tool permissions 2. Identify required tools 3. Configure agent tools - Configure MCP servers 1. Configure a GitHub remote MCP server 2. Add an MCP server as a tool to an agent 3. Configure MCP registries 4. Configure MCP allow lists - Integrate agents within development environments 1. Enable an agent to perform autonomous actions, including creating branches and pull requests 2. Configure an agent to use branch-based scope 3. Configure an agent to be invoked in a CI workflow 4. Evaluate the execution context for an agent 5. Configure an agent's scope to a specific repository 6. Configure an agent to handle environment-specific constraints
Prepare agent architecture and SDLC processes	15–20%	- Integrate agents into the software development lifecycle (SDLC) 1. Define inputs, outputs, and success criteria for agents 2. Identify steps for agents to perform 3. Identify and mitigate common anti-patterns in agents - Define boundaries between planning, reasoning, and action 1. Prevent agent action until the agent checks and approves 2. Validate agent plans 3. Configure agent planning to be distinct from agent execution 4. Configure an agent to output a structured plan - 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
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 - Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Prevent stale context 3. Share agent state - Persist agent state and manage context drift 1. Capture task progress and decisions as durable artifacts 2. Detect and correct drift during extended agent execution 3. Resume agent work without repeating steps or diverging from prior decisions
Orchestrate multi-agent coordination	15–20%	- Operate and manage multi-agent workflows 1. Apply an orchestration pattern to coordinate multiple agents 2. Configure agent isolation for parallel execution 3. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs - 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 - 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 - 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q17-Q22):

NEW QUESTION # 17
You have a repository that uses the GitHub Copilot coding agent and supports hooks stored under .github
/hooks.
You need a Shell command to run automatically whenever an agent execution fails.
Which type of hook should you use?

A. sessionEnd
B. agentStop
C. errorOccurred
D. postToolUse

Answer: C

Explanation:
The errorOccurred hook is designed to respond to failures during an agent session. It can invoke a shell command when an agent execution encounters an error, enabling automated capture of diagnostic information, cleanup actions, or controlled escalation.
A postToolUse hook runs after individual tool calls, whether or not the overall agent execution subsequently fails. It is suitable for logging or post-processing tool activity but does not specifically target an execution failure. A session-end event indicates that a session has ended; it does not necessarily represent an error condition. agentStop is not the defined hook required by the scenario.
Failure hooks should be used carefully. Their commands should collect only the required diagnostics and must avoid exposing secrets in logs. If the failure itself results from a prohibited operation, the hook should preserve the reason and relevant metadata so that a human can distinguish policy denial from a technical execution failure.
Study-guide topics: lifecycle hooks, failure handling, diagnostic collection, and accountability.

NEW QUESTION # 18
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. Connect an MCP server that exposes the internal documentation
B. Use .copilotignore to hide public APIs
C. Add the internal docs as copilot-instructions.md
D. Enable plan mode

Answer: A

Explanation:
An MCP server exposing the internal documentation is the appropriate solution because Model Context Protocol extends Copilot with information and capabilities located outside the repository ' s native context.
GitHub describes MCP as a mechanism for integrating Copilot with external systems, enabling it to obtain context or invoke functionality that would otherwise be unavailable from the codebase alone.
This architecture is appropriate for proprietary library documentation because the internal API knowledge can remain centrally maintained rather than being duplicated into every repository. The MCP integration can expose a controlled documentation lookup/search capability, allowing Copilot to retrieve relevant definitions, usage patterns, or internal API information when required.
Option A is inferior because copilot-instructions.md is designed for concise repository-specific instructions and conventions, not as a substitute for a potentially large external documentation corpus. GitHub recommends it for persistent guidance such as building, testing, and repository conventions. Option C would remove context rather than add proprietary knowledge. Plan mode affects workflow sequencing and does not provide new external information.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction; MCP-based context augmentation; external knowledge integration; tool-mediated retrieval.

NEW QUESTION # 19
You have a GitHub repository that uses the GitHub Copilot coding agent to run agent plans for assigned issues.
You discover that some issues contain ambiguous implementation steps.
You need to reduce the likelihood that Copilot executes plans based on ambiguous implementation steps.
What should you do?

A. Add acceptance criteria and directions to the issues before assigning the issues to the agent.
B. Mention @copilot in a pull request comment to request changes after the plan runs.
C. Add a copilot-setup-steps.yml file to preinstall dependencies in the agent environment.
D. Add comments to the issues after assigning the issues to the agent.

Answer: A

Explanation:
The most effective intervention occurs before the issue becomes an executable assignment. Acceptance criteria define the observable conditions that constitute success, while implementation directions establish boundaries and clarify how the work should proceed. Providing both before assignment reduces the need for the agent to infer missing requirements during planning.
Applied to this scenario, the issue should distinguish required outcomes from optional improvements and identify constraints that the implementation must respect. Where steps are ambiguous, the author should resolve the ambiguity or explicitly require clarification before execution. Reviewable criteria also give the agent and maintainers a shared basis for evaluating whether the resulting changes satisfy the original request.
Options A and D intervene after assignment or execution has begun. They may support later correction, but they do not prevent the initial plan from being based on unclear directions. Option C addresses dependency availability, which is a separate environmental concern.
The governing design principle is to make the task sufficiently definite before delegating action. Relevant curriculum topics include defining inputs and success criteria, validating agent plans, and separating preparation from execution.
References: GH-600 study guide , Responsible use of Copilot agents .

NEW QUESTION # 20
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 # 21
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 # 22
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