Microsoft GH-600 Exam Questions : Developing in Agentic AI Systems

Exam Code: GH-600
Exam Name: Developing in Agentic AI Systems
Updated: Sep 18, 2026
Q&As: 111 Questions and Answers

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

Microsoft Developing in Agentic AI Systems Sample Questions:

Question #1

You have a GitHub repository that uses GitHub Actions for CI on pull requests. The repository contains a Node.js app.
You have a GitHub Copilot coding agent that opens pull requests for backlog items, and your company requires automated checks for agent-generated changes.
You plan to standardize success criteria so that pull requests created by agents only succeed when unit tests pass and CodeQL analysis completes.
You need to configure a GitHub Actions workflow that runs on pull requests, executes unit tests, and performs CodeQL analysis.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Reveal Solution Discussion 0

Correct Answer:

Question #2

You have a GitHub Enterprise repository that uses the GitHub Copilot coding agent and opens draft pull requests for assigned issues.
Evaluation results show that the agent repeatedly opens pull requests that modify .github/workflows/*.yml to bypass failing checks instead of fixing the underlying code.
You need to ensure that the agent fixes the underlying code instead of bypassing the failing checks.
What should you do?

A. Disable GitHub Actions workflows for pull requests created by the agent.
B. Update the .gitignore file.
C. Create a branch ruleset.
D. Start the GitHub Copilot CLI by using --allow-all.

Reveal Solution Discussion 0

Correct Answer: C 🗳️

Explanation: Only visible for VCEPrep members. You can sign-up / login (it's free).

Question #3

While upgrading App1, the agent identifies 47 issues, including a security vulnerability, and 46 API incompatibilities across different projects.
Which two actions are unsafe to delegate to the agent and require human involvement? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Generate the assessment.md file.
B. Approve all Git commits.
C. Validate the assessment.md file for accuracy.
D. Validate whether the tasks.md file exists.
E. Review the plan.md file for dependencies.

Reveal Solution Discussion 0

Correct Answer: B,C 🗳️

Explanation: Only visible for VCEPrep members. You can sign-up / login (it's free).

Question #4

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.

Reveal Solution Discussion 0

Correct Answer:

Question #5

You have a GitHub repository that uses the GitHub Copilot coding agent.
The agent is assigned to a long-running issue and has already opened a draft pull request linked to the issue. The pull request timeline shows Copilot started work and the pull request description shows periodic status updates.
After 70 minutes, the pull request stops receiving new commits, and the agent session log indicates that the session has timed out. In a pull request comment thread, the agent then proposes changes that no longer match the latest repository guidance.
You need to resume execution in a way that reestablishes the correct context and produces new commits in the existing draft pull request.
What should you do?