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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Orchestrate multi-agent coordination	15-20%	- Detect and respond to multi-agent failures and degraded behavior 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 2. Identify failed, partial, or stalled agent executions 3. Respond to degraded behavior or coordination across agents - Manage the lifecycle of agents within multi-agent workflows 1. Add agents to existing multi-agent workflows 2. Update, reconfigure, or replace agents without disrupting active workflows 3. Retire agents while preserving auditability and workflow continuity - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Document key decisions, handoffs, and outcomes across agents 2. Configure multi-agent workflows to produce artifacts suitable for review and audit 3. Perform post-hoc analysis of multi-agent behavior - Operate and manage multi-agent workflows 1. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 2. Configure agent isolation for parallel execution 3. Apply an orchestration pattern to coordinate multiple agents
Topic 2: 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. Configure agents to produce inspectable artifacts within standard development tooling 3. Plan and implement the degree of agent autonomy, including guardrails - Define boundaries between planning, reasoning, and action 1. Configure an agent to output a structured plan 2. Prevent agent action until the agent checked and approved 3. Validate agent plans 4. Configure agent planning to be distinct from agent execution - 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
Topic 3: 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. Scope permissions and execution contexts to enforce least-privilege access 3. Preserve execution velocity by minimizing approvals that do not materially reduce risk 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 4: 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. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Align evaluation criteria with development intent 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. Revise instructions, workflows, or constraints 2. Refine tool usage and tool access 3. Refine memory usage
Topic 5: 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. 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 - Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Share agent state 3. Prevent stale context
Topic 6: Implement tool use and environment interaction	20-25%	- Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Enable an agent to perform autonomous actions, including creating branches and pull requests 3. Configure an agent to use branch-based scope 4. Configure an agent to handle environment-specific constraints 5. Evaluate the execution context for an agent 6. Configure an agent to be invoked in a CI workflow - Select and configure agent tools 1. Configure agent tool permissions 2. Identify required tools 3. Configure agent tools - Operate agents with safe execution paths and robust error handling 1. Implement traceability and accountability for agent actions 2. Implement retries 3. Implement rollbacks 4. Implement error handling 5. Implement escalation paths - Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure a GitHub remote MCP server 3. Configure the MCP registries 4. Configure MCP allow lists

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Microsoft Developing in Agentic AI Systems Sample Questions (Q71-Q76):

NEW QUESTION # 71
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 a copilot-setup-steps.yml file to preinstall dependencies in the agent environment.
B. Mention @copilot in a pull request comment to request changes after the plan runs.
C. Add acceptance criteria and directions to the issues before assigning the issues to the agent.
D. Add comments to the issues after assigning the issues to the agent.

Answer: C

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.
Reference:

NEW QUESTION # 72
A designated top-level instructions file is used by some agentic tools to describe overall repository purpose, build/test commands, and conventions in a tool-agnostic way (usable across multiple AI coding agents, not just Copilot). What is this file commonly called?

A. copilot-setup-steps.yml
B. copilot-instructions.md
C. agents.md
D. .copilotignore

Answer: C

Explanation:
agents.md (or AGENTS.md) is an emerging convention for providing agent-agnostic project context - build steps, structure, conventions -- that multiple AI coding tools (not just Copilot) can consume.

NEW QUESTION # 73
You use a GitHub Actions workflow to orchestrate a multi-agent draft-review process for pull requests. The workflow uploads a single combined review-package artifact at the end of each run.
You discover that the review-package artifact is occasionally missing from the workflow run history, and you suspect that a user deleted it.
You need to identify which user manually deleted a workflow run artifact and when the deletion occurred.
What should you use?

A. the merge commit history
B. the checks.delete_logs audit log event
C. the artifact.destroy audit log event
D. the pull request description and comment history

Answer: C

Explanation:
You should check the artifact.destroy audit log event.
This specific event tracks whenever a user or a token manually deletes a workflow run artifact. It provides exactly what you need to troubleshoot why your multi-agent review packages are disappearing.
Reference:
https://docs.github.com/en/organizations/managing-organization-settings/configuring-the-retention-period-for-github-actions-artifacts-and-logs-in-your-organization

NEW QUESTION # 74
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. errorOccurred
B. agentStop
C. postToolUse
D. sessionEnd

Answer: A

Explanation:
To automatically run a Shell command whenever a GitHub Copilot coding agent execution fails, you should use the errorOccurred (also referred to as onErrorOccurred) hook.
Hook Mechanics & ConfigurationGitHub Copilot agent hooks are defined using JSON configuration files placed in the .github/hooks/ directory.
Event Type: errorOccurred (or onErrorOccurred depending on your specific environment and version).
Execution Behavior: When an execution fails, the agent stops, triggers this hook, and passes detailed error metrics and session context as a JSON payload to the script's standard input (stdin).
Incorrect:
[Not B]
sessionEnd - Agent session completes or is terminated.
Reference:
https://awesome-copilot.github.com/learning-hub/automating-with-hooks/

NEW QUESTION # 75
You are evaluating how agent1 will behave after you implement the planned changes.
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:

NEW QUESTION # 76
......

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