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| Section | Weight | Objectives |
|---|---|---|
| Orchestrate multi-agent coordination | 15–20% | - Prevent conflicts and manage shared resources - Monitor and troubleshoot multi-agent execution - Design workflows for multiple agents - Define communication and handoff protocols |
| Implement tool use and environment interaction | 20–25% | - Connect agents to codebase, APIs, and external systems - Implement tools, custom actions, and MCP servers - Manage permissions and environment access - Configure and extend GitHub Copilot agents |
| Perform evaluation, error analysis, and tuning | 15–20% | - Diagnose failures, hallucinations, and unexpected behavior - Define metrics and quality standards for outputs - Optimize prompts, tools, and behavior through iteration - Test, validate, and compare agent results |
| Manage memory, state, and execution | 10–15% | - Scope and persist agent state correctly - Handle execution flow, retries, and interruptions - Choose memory types: short-term, long-term, external - Implement memory cleanup and expiration rules |
| Prepare agent architecture and SDLC processes | 15–20% | - Plan agent deployment, monitoring, and maintenance - Define agent purpose, scope, and success criteria - Design agent autonomy and decision boundaries - Integrate agents into software development lifecycle |
| Implement guardrails and accountability | 10–15% | - Enforce least privilege and security boundaries - Log actions, decisions, and changes for audit - Add validation, review, and approval gates - Ensure compliance, safety, and responsible use |
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問題 #95
You have a GitHub repository that uses three GitHub Copilot coding agents named agent1, agent2, and agent3.
During structured evaluation runs, agent1 frequently returns Markdown narratives instead of a machine-parsable result.
You need to ensure that agent1 consistently returns only a predefined JSON structure without affecting the output of the other agents.
Which file should you modify?
答案：A
解題說明：
The required change concerns one agent's behavior, making its custom agent profile the appropriate scope. The file .github/agents/agent1.agent.md defines instructions for agent1. Its Markdown instruction body can specify the required JSON structure, mandatory properties, allowed values, and prohibition of explanatory prose or Markdown fences.
Repository-wide instructions apply more broadly and could alter the behavior of agent2 and agent3, contrary to the requirement. The setup workflow prepares the execution environment rather than defining the agent's response contract. A file under .github/instructions uses instruction applicability rules; naming it after an agent does not automatically bind it exclusively to that agent.
The evaluation finding should be converted into a precise behavioral requirement and tested against representative inputs. For example, checks should distinguish valid JSON from text containing a JSON fragment and should reject missing fields or unexpected properties.
An agent instruction improves adherence but is not a mathematical guarantee of schema compliance. Production consumers should validate the returned object before using it. Relevant curriculum topics are revising instructions based on evaluation results and specifying expected output constraints.
Reference:
問題 #96
You are analyzing the following agent logs.
You need to classify the error in a report for your company's CTO.
How should you classify the error?
答案：D
解題說明：
This is a Tool misuse error (specifically, a configuration/implementation bug).The agent successfully passed the reasoning and context phases, but the environment's internal handler failed because the mcp_microsoftdocs_microsoftdocs_search tool was not properly registered or the IDE failed to process the tool call's schema.
Reference:
https://github.com/anthropics/claude-quickstarts/issues/345
問題 #97
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?
答案：A
解題說明：
The configuration must be changed on the agent that performs the delegation. Orchestrator is responsible for coordinating the workflow, so its allowed subagent list must include Summarizer. Adding the new agent to that list makes it an available delegation target alongside Planner, Implementer, and Reviewer.
An agent's agents property identifies the custom agents it can invoke as subagents. The tools property serves a different purpose: it identifies available capabilities such as reading files, editing code, or invoking agents. Adding a custom agent's name to the tools list does not register that agent as a callable subagent. The agent invocation tool must also be available; the scenario already establishes that Orchestrator delegates to other subagents.
Changing Reviewer's configuration would enable a relationship originating from Reviewer, rather than the requested direct invocation by Orchestrator. A handoff also represents a different interaction pattern from having the orchestrating agent invoke a subagent and receive its result.
Availability does not force execution on every task. Orchestrator's instructions should indicate when a summary is required and what information it should contain.
Study-guide topics: delegation permissions, orchestrator responsibilities, and specialized subagents. Reference: VS Code-Subagents.
問題 #98
You have multiple GitHub Copilot coding agents that run tasks concurrently.
You are monitoring the agents from the terminal by using the GitHub CLI.
An agent appears stalled.
You need to live stream the session log output.
What should you do?
答案：C
解題說明：
To live stream the session log output of a background agent, you must run the gh agent-task view
<task-id> --log --follow command.
Command Syntax
gh agent-task view <task-id> --log --follow
Parameter Breakdown
<task-id>: The unique identifier of the stalled agent task. If you do not know the ID, run gh agent- task list first to look it up.
--log: Instructs the GitHub CLI to pull the specific execution and reasoning logs rather than just a summary of the task state.
--follow: Tells the CLI to keep the connection open and live stream new log events to your terminal in real-time as they happen.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/manage-and-track-agents
問題 #99
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.
答案：B,D
解題說明：
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/
問題 #100
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