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Microsoft GH-600 Exam Syllabus Topics:

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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q77-Q82):

NEW QUESTION # 77
Drag and Drop Question
Your company uses GitHub Copilot Enterprise.
Developers use GitHub Copilot agent mode in Microsoft Visual Studio Code on their laptops and Copilot Chat on github.com when they are away from their laptops.
When switching between environments, the developers notice that agent workflows lose continuity because the tools available in Visual Studio Code are unavailable on github.com.
You need to ensure that the agent tools and state are available consistently across environments and can be used from any device without local setup.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action 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: Use a Github-hosted Model Context Protocol (MCP) server for cloud-based workflows.
To ensure tools and states remain identical whether developers are on local laptops or on github.com (or GitHub Mobile), you must deploy Copilot Cloud Agents paired with remote cloud sessions.
Utilize Cloud Sessions: Shift developers away from local-only agent workflows. Cloud sessions run agents inside isolated, GitHub-hosted cloud environments rather than relying on local machine resources. This permits developers to resume, steer, and monitor active workflows from any device using the browser or the GitHub Copilot App.
Box 2: Enable MCP servers in Copilot policy
To ensure that agent tools and states are available consistently across all environments (including github.com) and can be used from any device without local setup, you must Enable MCP servers in Copilot policy.
Centralized Enforcement: Enabling the Model Context Protocol (MCP) policy at the enterprise or organization level allows administrators to define a central registry of approved tools.
Cross-Environment Continuity: Once the policy is active and servers are configured in the repository or organization settings, the cloud-based Copilot environment (github.com) natively inherits those exact tools. This removes dependency on individual laptop configurations or local extension states.
Reference:
https://docs.github.com/en/copilot/how-tos/administer-copilot/manage-for-enterprise/manage-agents/enable-copilot-cloud-agent
https://docs.github.com/en/copilot/how-tos/provide-context/use-mcp-in-your-ide/extend-copilot-chat-with-mcp

NEW QUESTION # 78
You have a GitHub repository that contains an agent named Orchestrator.Orchestrator delegates work to the following specialized subagents:
- Planner reviews issues and creates a plan of action.
- Implementer writes code based on the plan of action.
- Reviewer reviews the code.
You create a new agent named Summarizer that produces a concise summary of the work performed by the other agents.
You need to ensure that Orchestrator can invoke Summarizer as part of its workflow.
What should you do?

A. In the YAML frontmatter of the Reviewer agent, add Summarizer to the agents list.
B. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
C. In the YAML frontmatter of the Reviewer agent, add a handoffs entry that points to the Summarizer agent.
D. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the tools list.

Answer: B

Explanation:
To ensure that Orchestrator can invoke the new Summarizer agent as part of its workflow, you should take the following action: In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
In GitHub Copilot custom agent configurations (defined in .agent.md files), the properties specified in the YAML frontmatter serve distinct purposes:
agents: This field acts as an explicit whitelist of other custom subagents that the parent agent is allowed to invoke and delegate work to. Adding Summarizer here registers it as an authorized subagent within the Orchestrator workflow.
Reference:
https://awesome-copilot.github.com/learning-hub/building-custom-agents/

NEW QUESTION # 79
You need to configure agent1 to support the planned changes.
What should you do?

A. Add Use all available tools to the .github/copilot-instructions.md file.
B. Add Use all available tools to the instructions in the agent configuration.
C. Delete line 05 from the agent configuration.
D. In the agent configuration, replace line 05 with the following: tools: []
E. Add the mcp-servers property to the agent configuration.

Answer: E

Explanation:
The mcp-servers property connects a custom agent to the Model Context Protocol servers required for its planned work. MCP configuration makes defined external tools and services available to the agent in a controlled, declarative manner.
Adding a broad natural-language instruction such as "Use all available tools" does not configure a server connection, authenticate a tool, or define which external capabilities are available. It is also poor least-privilege design because it encourages unrestricted tool use without establishing a technical permission boundary.
Setting tools: [] removes all configured tools and would prevent the agent from using required capabilities. Deleting the tools line could cause the agent to inherit or expose a broader default tool set than intended. Repository-wide instructions can guide behavior, but they do not replace the agent-specific configuration needed to associate MCP servers with that agent.
A well-designed configuration identifies only the MCP servers necessary for the role, supplies secrets through approved environment mechanisms, and limits the resulting tools to the agent's defined task.
Study-guide topics: MCP integration, agent tool configuration, and least-privilege environment access.

NEW QUESTION # 80
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. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.
B. Commit trace.json and plan.md back to the repository from agent-run.
C. Use dependency caching to store trace.json and plan.md.
D. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.

Answer: D

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 # 81
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. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.
B. Commit trace.json and plan.md back to the repository from agent-run.
C. Use dependency caching to store trace.json and plan.md.
D. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.

Answer: D

NEW QUESTION # 82
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