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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Perform evaluation, error analysis, and tuning	15–20%	- Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Revise instructions, workflows, or constraints 3. Refine memory usage - 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
Manage memory, state, and execution	10–15%	- 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 - 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 stale context 2. Share agent state 3. Prevent conflicting context
Prepare agent architecture and SDLC processes	15–20%	- 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 - 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 checks and approves 4. Configure an agent to output a structured plan - 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
Implement tool use and environment interaction	20–25%	- Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Configure an agent to be invoked in a CI workflow 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 handle environment-specific constraints 6. Configure an agent to use branch-based scope - Operate agents with safe execution paths and robust error handling 1. Implement rollbacks 2. Implement traceability and accountability for agent actions 3. Implement error handling 4. Implement retries 5. Implement escalation paths - Configure MCP servers 1. Configure MCP allow lists 2. Add an MCP server as a tool to an agent 3. Configure a GitHub remote MCP server 4. Configure MCP registries - Select and configure agent tools 1. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions
Orchestrate multi-agent coordination	15–20%	- Detect and respond to multi-agent failures and degraded behavior 1. Identify failed, partial, or stalled agent executions 2. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 3. Respond to degraded behavior or coordination across agents - Manage the lifecycle of agents within multi-agent workflows 1. Retire agents while preserving auditability and workflow continuity 2. Add agents to existing multi-agent workflows 3. Update, reconfigure, or replace agents without disrupting active workflows - Operate and manage multi-agent workflows 1. Configure agent isolation for parallel execution 2. Apply an orchestration pattern to coordinate multiple agents 3. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs - 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
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. 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 (Q68-Q73):

NEW QUESTION # 68
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?

A. Close and reopen the issue.
B. Create a new issue and assign the issue to Copilot.
C. Select Approve and run workflows in the pull request merge box.
D. Post a pull request comment that mentions @copilot.

Answer: D

Explanation:
A comment mentioning @copilot on the existing pull request provides the direct continuation mechanism. GitHub documents that this can start a new agent session and, by default, push additional commits to the pull request's branch. The comment should explicitly identify the latest repository guidance and the work that remains, correcting the context that produced the outdated proposal.
This approach preserves the existing branch, accumulated changes, review discussion, and relationship to the original issue. It also gives the agent a clear instruction tied to the artifact that must be updated. The commenter must have the required repository write access.
Creating another issue would establish a separate assignment and could duplicate work. Approve and run workflows authorizes GitHub Actions validation; it does not itself resume the coding agent's implementation session. Closing and reopening the issue is likewise not the direct pull-request continuation control.
The useful recovery action therefore combines a new execution trigger with an explicit statement of current intent. Restarting without correcting the outdated requirement could reproduce the same drift.
Relevant curriculum topics are resuming execution, restoring task context, and maintaining continuity without discarding prior work.
Reference:

NEW QUESTION # 69
Drag and Drop Question
You have a GitHub Enterprise Cloud Organization that uses the GitHub Copilot coding agent to resolve issues asynchronously.
When an issue is assigned to GitHub Copilot, the agent creates a draft pull request, but your team cannot always tell whether the agent is actively working, has completed its session, or is awaiting workflow approval.
Which execution context does each signal indicate? To answer, drag the appropriate context to the correct signals. Each signal 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: The agent acknowledges the assignment and will create the draft pull request.
When an issue is assigned to the GitHub Copilot coding agent, the eyes emoji reaction indicates that the agent has acknowledged the task and is actively starting work in the background.
Box 2: The agent session is actively running and generating live logs.
The signal indicating that "the pull request timeline shows Copilot started work" means that the agent session is actively running and generating live logs.
When a pull request timeline shows that Copilot started work, it indicates that the execution context is actively working.
Actively working: Indicated when the pull request timeline explicitly logs that Copilot started work or updates the PR body with a list of in-progress sub-tasks.
Box 3: A human must manually approve and run the workflow.
When a draft pull request exists but GitHub Actions checks are not running, it indicates that the execution context is awaiting workflow approval.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/kick-off-a-task

NEW QUESTION # 70
You want to prevent GitHub Copilot from ever suggesting completions or making edits inside a directory containing sensitive credentials templates. What should you configure?

A. Branch protection rules
B. A CODEOWNERS file
C. A repository ruleset
D. A .copilotignore file

Answer: D

NEW QUESTION # 71
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
agent-run that generates trace.json and plan.md
review that waits for human approval before continuing
deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?

A. Use dependency caching to store trace.json and plan.md.
B. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.
C. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.
D. Commit trace.json and plan.md back to the repository from agent-run.

Answer: C

Explanation:
Workflow artifacts provide the required combination of retained execution evidence and file transfer between jobs. The agent-run job uploads trace.json and plan.md, associating both files with the workflow run. Reviewers can access the resulting artifact through the run's GitHub Actions interface, while downstream jobs download it to their own execution environments.
This is necessary because separate jobs do not automatically share a local filesystem. Declaring a dependency establishes execution order but does not transfer the generated files. The review and deployment stages therefore need explicit artifact downloads in addition to their appropriate job dependencies.
Committing transient traces and plans introduces repository changes solely to transport run-specific evidence. A network share adds external infrastructure and does not directly satisfy the requirement for standard Actions artifact access. Dependency caching is intended for reusable inputs such as downloaded dependencies; it is not the appropriate mechanism for authoritative outputs from a particular agent run.
Artifacts preserve the plan and trace that informed the review, allowing subsequent deployment activity to use the corresponding files. Relevant curriculum topics are inspectable agent artifacts, observability, and human intervention.
Reference:

NEW QUESTION # 72
You need finer control, selecting specific files and describing precise natural-language changes to apply, rather than letting the agent decide the full scope of changes. Which Copilot Chat mode should you use?

A. Edit mode
B. Plan mode
C. Agent mode
D. Ask mode

Answer: A

Explanation:
Edit mode is designed for targeted, developer-directed changes: you select the files in scope and describe the specific modification, giving you more granular control than the fully autonomous agent mode.

NEW QUESTION # 73
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