GH-600 Exam Question | Exam GH-600 Review

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Prepare agent architecture and SDLC processes	15-20%	- Define boundaries between planning, reasoning, and action 1. Configure agent planning to be distinct from agent execution 2. Validate agent plans 3. Prevent agent action until the agent checked and approved 4. Configure an agent to output a structured plan - 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 - 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
Perform evaluation, error analysis, and tuning	15-20%	- Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Refine memory usage 3. Revise instructions, workflows, or constraints - 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. Specify expected outcomes and operational constraints for agent tasks 2. Align evaluation criteria with development intent 3. Generate evaluation signals by using automated scanning tools 4. Identify qualitative and quantitative evaluation signals to evaluate agents
Implement guardrails and accountability	10-15%	- Implement guardrails and human-in-the-loop workflows 1. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 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. Identify the subset of actions that require human judgment 5. Block actions that violate defined security, compliance, or Responsible AI policies - 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%	- Configure MCP servers 1. Configure a GitHub remote MCP server 2. Add an MCP server as a tool to an agent 3. Configure MCP allow lists 4. Configure the MCP registries - Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement error handling 3. Implement traceability and accountability for agent actions 4. Implement rollbacks 5. Implement escalation paths - Integrate agents within development environments 1. Configure an agent to use branch-based scope 2. Configure an agent's scope to a specific repository 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 handle environment-specific constraints 6. Evaluate the execution context for an agent - Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools
Manage memory, state, and execution	10-15%	- Implement agent memory strategies 1. Define memory expiration, pruning, and reset rules 2. Choose between short-term, long-term, and external memory 3. Scope agent memory to task-relevant information - Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Share agent state 3. Prevent stale context - 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
Orchestrate multi-agent coordination	15-20%	- 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 - Manage the lifecycle of agents within multi-agent workflows 1. Update, reconfigure, or replace agents without disrupting active workflows 2. Add agents to existing multi-agent workflows 3. Retire agents while preserving auditability and workflow continuity - 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 - 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q36-Q41):

NEW QUESTION # 36
You have a GitHub repository that uses a GitHub Actions workflow to run an agent-driven change plan as part of a CI pipeline. The workflow generates an artifact named plan.json that includes a field named risk. risk has possible values of low, medium, or high.
You need to ensure that a human must confirm the execution of the workflow when risk is medium or high. The workflow must proceed automatically only when risk is low.
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:

NEW QUESTION # 37
A developer uses the GitHub Copilot CLI in plan mode.
Copilot produces a plan.
What does Copilot do next?

A. begins the implementation
B. creates a branch named /copilot/plan in the repository
C. opens a pull request that has the plan as a comment
D. saves the plan to plan.md

Answer: D

Explanation:
In plan mode, after GitHub Copilot CLI analyzes your codebase and gathers requirements, it saves the plan to plan.md in your session folder.
What Happens Next?
1. Saves to plan.md: It writes the structured implementation plan with checkable tasks into a file called plan.md.
2. Waits for Approval: Copilot pauses and waits for your review. It will not begin implementation or make code modifications until you explicitly approve the strategy.
3. Execution: Once approved, you can hand the plan off to Copilot's agent/autopilot mode to begin the implementation.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-cli/cli-best-practices

NEW QUESTION # 38
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. Add comments to the issues after assigning the issues to the agent.
C. Add acceptance criteria and directions to the issues before assigning the issues to the agent.
D. Mention @copilot in a pull request comment to request changes after the plan runs.

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 # 39
You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
You review the session log and the pull request.
What are two automated evaluation signals generated by the coding agent's built-in scanning tools? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. CodeQL findings that identify security vulnerabilities in the generated code
B. linting errors from the repository's CI pipeline configured in GitHub Actions
C. the detection of hardcoded secrets, such as API keys and tokens
D. comments from Copilot code review that suggest improvements to code patterns
E. the median time to merge metric reported on the Copilot usage metrics dashboard

Answer: A,C

Explanation:
The two automated evaluation signals generated by the coding agent's built-in scanning tools are:
CodeQL findings that identify security vulnerabilities in the generated code.
The detection of hardcoded secrets, such as API keys and tokens.
Reference:
https://itacademy.com.ua/en/articles/2026-06-11/security-validation-third-party-coding-agents-github/

NEW QUESTION # 40
You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
You review the session log and the pull request.
What are two automated security scanning signals available for the pull request? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. CodeQL findings that identify security vulnerabilities in the generated code
B. The median time to merge metric reported on the Copilot usage metrics dashboard
C. Linting errors from the repository's CI pipeline configured in GitHub Actions
D. The detection of hardcoded secrets, such as API keys and tokens
E. Dependency alerts that identify vulnerable package versions in the repository

Answer: A,D

Explanation:
CodeQL findings and secret-detection results are the intended selections. CodeQL analyzes code for patterns associated with vulnerabilities and programming errors. Secret scanning identifies sensitive material, including supported credential and token patterns, that may have been introduced into the generated changes. Both provide concrete security evidence that a reviewer can evaluate alongside the proposed fix.
Linting generally evaluates coding conventions and selected correctness rules; a generic linting failure is not necessarily a security finding. Median time to merge measures delivery performance rather than the security of a particular change.
The wording of option D creates an important technical distinction. Dependency vulnerability detection is also a genuine automated security signal. GitHub explicitly documents dependency analysis alongside CodeQL and secret scanning in the coding agent's security checks. Repository-level dependency alerts, however, may concern existing vulnerabilities rather than changes introduced by this pull request. B and C are the clearest intended pair, but D cannot be dismissed as unrelated to security.
Reviewers should determine whether each finding applies to the proposed changes and whether the underlying vulnerability has actually been resolved.
Relevant curriculum topics are automated evaluation signals, security scanning, and evidence-based assessment of agent outputs.
Reference:

NEW QUESTION # 41
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