Real Microsoft GH-600 Questions & Latest GH-600 Test Labs

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Orchestrate multi-agent coordination	15–20%	- 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 - 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 - Operate and manage multi-agent workflows 1. Apply an orchestration pattern to coordinate multiple agents 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 3. Configure agent isolation for parallel execution - 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
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. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 3. Block actions that violate defined security, compliance, or Responsible AI policies 4. Scope permissions and execution contexts to enforce least-privilege access 5. Identify the subset of actions that require human judgment
Manage memory, state, and execution	10–15%	- Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Share agent state 3. Prevent conflicting context - Implement agent memory strategies 1. Scope agent memory to task-relevant information 2. Choose between short-term, long-term, and external memory 3. Define memory expiration, pruning, and reset rules - 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
Prepare agent architecture and SDLC processes	15–20%	- Configure observability and control for autonomous agents 1. Configure agents to produce inspectable artifacts within standard development tooling 2. Plan and implement the degree of agent autonomy, including guardrails 3. Configure human intervention for autonomous agents without slowing delivery - 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 - Define boundaries between planning, reasoning, and action 1. Prevent agent action until the agent checks and approves 2. Configure an agent to output a structured plan 3. Validate agent plans 4. Configure agent planning to be distinct from agent execution
Perform evaluation, error analysis, and tuning	15–20%	- Define success criteria and evaluation signals for agent tasks 1. Specify expected outcomes and operational constraints for agent tasks 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Generate evaluation signals by using automated scanning tools 4. Align evaluation criteria with development intent - Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine tool usage and tool access 3. Refine memory usage - 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
Implement tool use and environment interaction	20–25%	- Operate agents with safe execution paths and robust error handling 1. Implement escalation paths 2. Implement traceability and accountability for agent actions 3. Implement retries 4. Implement rollbacks 5. Implement error handling - Integrate agents within development environments 1. Configure an agent to use branch-based scope 2. Evaluate the execution context for an agent 3. Enable an agent to perform autonomous actions, including creating branches and pull requests 4. Configure an agent to handle environment-specific constraints 5. Configure an agent's scope to a specific repository 6. Configure an agent to be invoked in a CI workflow - Select and configure agent tools 1. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions - Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure MCP registries 3. Configure a GitHub remote MCP server 4. Configure MCP allow lists

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Quiz 2026 Perfect GH-600: Real Developing in Agentic AI Systems Questions

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Microsoft Developing in Agentic AI Systems Sample Questions (Q58-Q63):

NEW QUESTION # 58
You have a custom agent profile file named test-agent.agent.md that contains the following YAML frontmatter:
---
name: test-agent
description: Custom agent description
tools: ['tool-a', 'tool-b']
---
In the same repository, you have an MCP configuration file named mcp.json.
You need to ensure that the GitHub Model Context Protocol (MCP) server is available to test the agent. The solution must allow only the Copilot toolset.
What should you do?

A. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/copilot'].
B. Replace line 04 with tools: ['copilot/*'].
C. Replace "servers": with "mcp-servers": in mcp.json.
D. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/*'].

Answer: B

Explanation:
The copilot/* toolset grants the agent access to the Copilot-provided tools while avoiding unrelated tool groups. This directly meets the requirement to allow only the Copilot toolset for the test agent.
Keeping tool-a and tool-b would expand access beyond the stated requirement. Using github/* would expose the broader GitHub tool group rather than limiting the profile to the Copilot toolset. The MCP configuration retains its server structure; changing the server property name does not configure which tools the agent may use.
Toolsets are an important control surface for specialized agents. They let an architect define a narrow capability set for testing, planning, review, or implementation roles. The selected toolset should still be backed by an available and authorized MCP configuration; naming a toolset does not bypass server authentication or policy restrictions.
Study-guide topics: MCP toolsets, custom agent capabilities, and least-privilege tool access.

NEW QUESTION # 59
You are evaluating the logs of the multi-agent workflow in repo2.
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 # 60
Drag and Drop Question
You have a GitHub repository that uses a custom GitHub Copilot coding agent defined in the rollout-bot.agent.md file.
You need to update a workflow so that agent-profile changes can be rolled back by reverting a single commit and rerunning the workflow. The workflow must check out the exact commit being deployed and apply the agent profile from the repository at that commit.
How should you complete the workflow? 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:
Box 1: inputs.ref
The specific commit being targeted.
Box 2: agents/rollout-bot.agent.md
The profile file to validate
Box 3: ${{ github.sha }}
The exact commit ref that the checkout action fetched
Reference:
https://octopus.com/devops/github-actions/github-actions-workflows/

NEW QUESTION # 61
Drag and Drop Question
You have a GitHub repository that runs an agentic software development lifecycle (SDLC) workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel, and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors, and then fan in the results to a final executor.
How should you complete the workflow definition? 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:
Box 1: needs
Needs & [spec_analyzer, risk_reviewer] forces plan_merger to wait until both the analysis and risk review jobs have successfully executed in parallel, completing the fan-out portion of your pipeline.
Box 2: [spec_analyzer, risk_reviewer]
Box 3: concurrency
The top-level key prevents race conditions by grouping active workflow runs together. Using
${{ github.run_id }} (replacing the typo ${{ github.reg }}) correctly locks concurrent executions for the specific pipeline run.
Reference:
https://github.com/openclaw/openclaw/issues/38433

NEW QUESTION # 62
You have a GitHub Enterprise Cloud organization that uses a custom coding agent to run GitHub Actions workflows that create branches, open pull requests, and merge changes after required checks pass.
You need to log all agent-initiated actions and ensure that the logs are retained for two years.
What should you do?

A. Configure audit log streaming.
B. Enable log forwarding.
C. Enable Git events in the audit log.

Answer: A

Explanation:
Audit log streaming is the correct choice because it exports audit events to an external destination where the organization can apply its required two-year retention policy. This captures agent-initiated activity as part of the organization's auditable GitHub events.
Standard audit-log retention in GitHub may not meet a long-term evidence requirement on its own. Streaming allows the organization to store and search the exported events in a SIEM, data lake, or logging platform with retention controls aligned to compliance policy.
Log forwarding is not the required GitHub audit mechanism in this scenario. Enabling Git events can increase event visibility, but it does not independently provide the requested long-term retention of all relevant agent-initiated actions.
The external destination should preserve actor details, timestamps, repository identifiers, action types, and correlation information needed to reconstruct agent activity during an investigation.
Study-guide topics: accountability, audit log streaming, retention, and agent activity traceability.

NEW QUESTION # 63
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