Microsoft GH-600 Study Guide | GH-600 Exam Review

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prepare agent architecture and SDLC processes	15–20%	- 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 - 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 - 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
Topic 2: Implement tool use and environment interaction	20–25%	- Operate agents with safe execution paths and robust error handling 1. Implement traceability and accountability for agent actions 2. Implement escalation paths 3. Implement rollbacks 4. Implement retries 5. Implement error handling - Integrate agents within development environments 1. Configure an agent to use branch-based scope 2. Configure an agent to handle environment-specific constraints 3. Evaluate the execution context for an agent 4. Enable an agent to perform autonomous actions, including creating branches and pull requests 5. Configure an agent to be invoked in a CI workflow 6. Configure an agent's scope to a specific repository - 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 a GitHub remote MCP server 3. Configure MCP allow lists 4. Configure MCP registries
Topic 3: Manage memory, state, and execution	10–15%	- Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Prevent stale context 3. Share agent state - 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
Topic 4: 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 - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Document key decisions, handoffs, and outcomes across agents 2. Perform post-hoc analysis of multi-agent behavior 3. Configure multi-agent workflows to produce artifacts suitable for review and audit - 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. 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
Topic 5: Perform evaluation, error analysis, and tuning	15–20%	- Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine memory usage 3. Refine tool usage and tool access - Define success criteria and evaluation signals for agent tasks 1. Identify qualitative and quantitative evaluation signals to evaluate agents 2. Generate evaluation signals by using automated scanning tools 3. Specify expected outcomes and operational constraints for agent tasks 4. Align evaluation criteria with development intent - 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
Topic 6: 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. Identify the subset of actions that require human judgment 3. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 4. Preserve execution velocity by minimizing approvals that do not materially reduce risk 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q88-Q93):

NEW QUESTION # 88
You have a GitHub Enterprise Cloud Organization that uses the GitHub Copilot coding agent.
Copilot creates a draft pull request for an assigned issue, and the pull request timeline shows Copilot started work.
After 70 minutes, the agent session log stops updating, and the pull request body status stops changing.
You need to restart the agent so that it continues the task from the issue context and produces new commits to the existing draft pull request.
What should you do?

A. Unassign the issue from Copilot, and then reassign the issue to Copilot.
B. Wait for the agent to complete.
C. Select Approve and run workflows in the pull request merge box.
D. Merge the draft pull request and mention @copilot on the merged pull request.

Answer: A

Explanation:
To restart the background session and force the agent to resume its task, unassign the issue from GitHub Copilot and then reassign it to Copilot.
This specific operational cycle terminates the frozen cloud background process and launches a fresh agent session. Because a draft pull request already exists and is bound to the issue context, the newly initiated session automatically detects the linked branch, picks up the previous implementation plan, and begins pushing new commits directly to that existing draft PR.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent

NEW QUESTION # 89
You assigned an issue to the Copilot coding agent, and it opened a pull request. You want to inspect exactly what code changes were made before merging. Which CLI slash command lets you view the change set directly in the terminal?

A. /compact
B. /context
C. /plan
D. /diff

Answer: D

Explanation:
/diff shows the file-level changes the agent has made during the session, letting you review modifications before committing or merging, without leaving the terminal.

NEW QUESTION # 90
You have a GitHub Enterprise Cloud repository that uses GitHub Actions for CI and requires pull requests for all changes.
You are planning a GitHub Actions workflow where a coding agent drafts implementation changes and tests from GitHub issues, and an automated review runs before human review.
You need the agent to draft changes from an assigned issue, open a pull request, and add an automated review to the pull request before requesting a human review.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 91
Before App1 is upgraded, you need to verify each individual upgrade step and whether all tests have passed.
Which file should you use?

A. <project>/.github/plan.md
B. <project>/.mcp/agent.md
C. <project>/.github/upgrades/{scenarioId}/tasks.md
D. <project>/.github/upgrades/{scenarioId}/assessment.md

Answer: C

Explanation:
The tasks.md file is the execution-tracking document for the upgrade scenario. It records individual tasks, their validation criteria, and their completion status. Microsoft's upgrade walkthrough specifically directs users to review this file when checking the status of every upgrade step.
A task can include several concrete verification activities, such as restoring dependencies, building the solution, correcting compilation errors, running the test suite, and rerunning tests after fixes. Reviewing these entries reveals whether implementation has merely been attempted or has satisfied the defined checks.
The assessment identifies upgrade issues and scope; it does not serve as the live record of completed work. A plan describes intended actions and ordering, which also differs from evidence that execution and testing succeeded.
The phrase "before App1 is upgraded" requires temporal care. Before execution, tasks.md identifies the steps and checks that must be performed. It cannot prove that future tests have already passed. Completion and test results become available as the agent executes and updates the tasks.
Study-guide topics: execution validation, task status, and acceptance criteria. Reference: Microsoft Learn-Execute and verify a Copilot upgrade.

NEW QUESTION # 92
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. Mention @copilot in a pull request comment to request changes after the plan runs.
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. Add a copilot-setup-steps.yml file to preinstall dependencies in the agent environment.

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 # 93
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