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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Orchestrate multi-agent coordination | 15–20% | - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
|
| Topic 2: Perform evaluation, error analysis, and tuning | 15–20% | - Define success criteria and evaluation signals for agent tasks
|
| Topic 3: Prepare agent architecture and SDLC processes | 15–20% | - Configure observability and control for autonomous agents
|
| Topic 4: Implement guardrails and accountability | 10–15% | - Implement guardrails and human-in-the-loop workflows
|
| Topic 5: Implement tool use and environment interaction | 20–25% | - Configure MCP servers
|
| Topic 6: Manage memory, state, and execution | 10–15% | - Ensure continuity of agent memory and state across tools and environments
|
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NEW QUESTION # 72
Hotspot Question
Your company uses GitHub Copilot custom agents in Microsoft Visual Studio Code.
The company also uses the Copilot coding agent on GitHub issues.
You have a file named .planner.agent.md that defines an agent named planner.planner has tools set to ['search', 'read', 'fetch']. There are explicit instructions NOT to write or modify any code. The file also defines a handoff labeled Start Implementation to an agent named implementer and sets send to false.
Developers report that after the planner agent produces a plan, implementation sometimes starts immediately in the same conversation, and code changes appear without an explicit agent switch.
When Copilot-created pull requests stall, maintainers review the pull request timeline and session logs. Several stalled sessions show outbound network commands blocked by a firewall, and the repositories do NOT contain a .github/copilot-instructions.md file.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
The configuration of the planner agent alone does not explicitly or fully prevent code changes from being produced automatically in the same conversation.
Box 2: No
No, removing the search tool will not resolve the issue, because the planner agent is not the one executing the network requests or writing the code.
The underlying problem is that the handoff configuration has an logic error (send: false), which prevents a clean, explicit agent migration. Because of this, the implementer agent takes over implicitly within the same conversation session, executing background code changes and attempting blocked internet calls.
Box 3: No
No, adding a .github/copilot-instructions.md file demanding tests and linters will not mitigate the stalled pull requests.
The root cause of your stalling pull requests is an underlying network connectivity issue, not a procedural omission by the agent.
Reference:
https://docs.github.com/en/copilot/concepts/agents/cloud-agent/about-custom-agents
NEW QUESTION # 73
You have a repository that uses the GitHub Copilot coding agent and supports hooks stored under .github/hooks.
You need a Shell command to run automatically whenever an agent execution fails.
Which type of hook should you use?
Answer: D
Explanation:
The errorOccurred hook is designed to respond to failures during an agent session. It can invoke a shell command when an agent execution encounters an error, enabling automated capture of diagnostic information, cleanup actions, or controlled escalation.
A postToolUse hook runs after individual tool calls, whether or not the overall agent execution subsequently fails. It is suitable for logging or post-processing tool activity but does not specifically target an execution failure. A session-end event indicates that a session has ended; it does not necessarily represent an error condition. agentStop is not the defined hook required by the scenario.
Failure hooks should be used carefully. Their commands should collect only the required diagnostics and must avoid exposing secrets in logs. If the failure itself results from a prohibited operation, the hook should preserve the reason and relevant metadata so that a human can distinguish policy denial from a technical execution failure.
Study-guide topics: lifecycle hooks, failure handling, diagnostic collection, and accountability.
NEW QUESTION # 74
You have a private GitHub repository that has Copilot memory enabled.
Several developers who have write access to the repository make changes across multiple branches, including creating some pull requests that are later closed without merging.
Your team needs to understand how GitHub Copilot ensures that only task-relevant, up-to-date information influences code suggestions, even when older memories exist.
How does Copilot manage memories?
Answer: B
Explanation:
GitHub Copilot Memory relies on a mechanism called just-in-time verification to ensure older or unmerged code changes do not pollute its code suggestions.
Reference:
https://docs.github.com/en/copilot/concepts/agents/copilot-memory
NEW QUESTION # 75
Case Study 1 - Contoso, Ltd
Overview
Contoso Ltd. is a software development company located in the United States.
Existing Environment
GitHub Environment
Contoso uses GitHub Enterprise and assigns GitHub Copilot Pro+ licenses to its developers. The developers use Microsoft Visual Studio Code as their IDE.
Contoso has a customer portal. The code for the portal is stored in a GitHub repository named repo1that contains the following:
- A custom agent named agent1 that includes instructions to review specs related to best practices
- A custom instruction file named validate-instructions.md that is used to validate tone of voice and applies to all .md and .txt files
- A custom instruction file named codereview.instructions.md that is used by the Copilot coding agent but is excluded for use by the Copilot code review repo1 has the following structure:
- The front-end is stored in the /frontend folder.
- The API logic is stored in the /api folder.
Contoso has a second repository named repo2 that contains a legacy .NET application named App1 built by using .NET 6. repo2 has a multi-agent workflow for modernization tasks.
Contoso enables the Model Context Protocol (MCP) registry and allows the Microsoft Learn MCP Server. Every developer must configure their own connection to the Learn MCP Server.
Problem Statements
The developers working in repo1 report that the Microsoft Learn documentation is NOT being retrieved when they attempt to validate a design by using agent1.
The testing team at Contoso identifies that the customer portal uses inconsistent UI styles, which leads to customer confusion and branding issues. The UI inconsistencies stem from variations in the folder structure.
Agent Logs
You have the following logs for the multi-agent workflow used in repo2.
Requirements
Planned Changes
Contoso plans to have all agents and developers in repo1use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
Contoso plans to leverage AI-powered coding agents to implement new portal features and pages.
Technical Requirements
App1 must be upgraded to .NET 10. A previous upgrade attempt was started by using the Copilot modernization agent, but the attempt was never finalized.
You plan to retry the upgrade. You must first analyze App1 by using AI, and then generate a report that contains breaking changes and deprecated patterns before retrying the upgrade.
All AI-generated code for UI styling must adhere to a predefined folder structure.
The architects at Contoso need help building implementation plans for repo1. The company wants to implement a new agent named agent2 to analyze the code base and the code requirements, and then respond with a detailed plan. The agent must NOT be able to edit files or run local commands.
The developers must be able to delegate work to the Copilot coding agent by assigning issues to the agent.
Hotspot Question
You need to implement agent2 to meet the technical requirements.
How should you complete the YAML configuration? 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:
Explanation:
Box 1: 'search',
Search allows the agent to look for specific keywords, classes, or patterns across your repository to understand the current structure and code requirements.
Box 2: 'read'
Read grants the agent read-only permission to examine the full contents of the codebase files without having the capability to alter them.
Reference:
https://github.com/github/copilot-cli/issues/1663
NEW QUESTION # 76
You are analyzing the following agent logs.
2026-03-19 21:00:59.905 [info] ccreq:6343bff1.copilotmd | success | gpt-5.3-codex | 10385ms | [panel
/editAgent]
2026-03-19 21:01:09.644 [info] ccreq:4612e90d.copilotmd | success | gpt-5.3-codex | 9806ms | [panel
/editAgent]
2026-03-19 21:01:09.856 [info] [ToolCallingLoop] Stop hook result: shouldContinue=false, reasons=undefined
2026-03-19 21:01:30.816 [info] ccreq:9dcd0468.copilotmd | success | gpt-5.3-codex | 5489ms | [panel
/editAgent]
2026-03-19 21:01:31.475 [error] Error from tool mcp_microsoftdocs_microsoft_docs_search with args
{ " query " : " VS Code custom agent name from frontmatter and filename behavior " }:
Cannot read properties of undefined (reading ' invoke ' ):
TypeError: Cannot read properties of undefined (reading ' invoke ' )
You need to classify the error in a report for your company's CTO.
How should you classify the error?
Answer: C
Explanation:
C is the closest available classification because the explicit failure occurs during a tool invocation. The log identifies the Microsoft documentation search tool and records a JavaScript TypeError involving an undefined object's invoke property. The preceding successful model requests do not establish successful tool execution; model generation and external tool invocation are separate operations.
For an operational report, the more precise description is tool invocation or integration failure . The evidence does not prove that the agent selected an inappropriate tool or supplied invalid arguments. An undefined invocation object can also result from an extension defect, stale tool registration, or another runtime integration problem. Therefore, "tool misuse" should be understood as the question's broad tool-related category rather than a proven agent-behavior defect.
A network classification would require supporting evidence such as connection failure, DNS errors, or an HTTP response. A reasoning or context classification would require evidence that the agent's interpretation or supplied information caused the failure.
The next diagnostic step is to inspect the MCP server's output and registration state.
Relevant curriculum topics are classifying failure causes and distinguishing model behavior from tool and environment failures.
Reference: Troubleshooting MCP servers in Visual Studio Code .
NEW QUESTION # 77
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