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| Section | Weight | Objectives |
|---|---|---|
| Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
| Implement agents and multi-agent systems | 30% | - Build agents with Azure AI tools and frameworks
|
| Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
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質問 # 106
An agent working through the Copilot coding agent in GitHub Actions needs additional build dependencies installed before it can run tests. Where should you define this setup?
正解：B
解説：
copilot-setup-steps.yml defines a custom setup workflow (e.g., installing dependencies, configuring environment variables) that runs before the coding agent starts its task, ensuring it has the environment it needs.
質問 # 107
Hotspot Question
You have a GitHub repository that uses GitHub Copilot Chat in Microsoft Visual Studio Code.
Custom agents are stored in the repository under version control.
Your team uses a multi-agent workflow where a planner agent produces an implementation plan that is then handed off to an implementation agent to make changes.
Recent prompts cause the planner agent to start editing files and running commands before the plan is approved.
You need to configure the planner agent to meet the following requirements:
- Use only read-only tools.
- Hand off to the implementation agent only after the plan is approved.
How should you configure the agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: ['search','read','fetch']
This is the correct tool list configuration. It restricts the planner agent to strictly read-only capabilities, preventing it from editing code or executing state-changing commands before approval.
Box 2: true
Setting Send: true tells GitHub Copilot Chat to automatically transfer control and send the prompt payload to the implementer agent immediately upon user approval of the plan.
Reference:
https://medium.com/@gareth.hallberg_55290/porting-a-claude-code-command-to-vs-code-copilot-d114b338f3e0
質問 # 108
You have a GitHub repository that uses GitHub Actions to validate pull requests opened by the GitHub Copilot coding agent. The workflow runs unit tests and a linter on pull request triggers, and Copilot opens draft pull requests on dedicated branches while iterating by using commits.
You discover that when multiple Copilot sessions push updates to the same pull request branch in quick succession, multiple workflow runs execute concurrently.
You need to enable parallel workflow executions across different pull request branches.
How should you configure workflow-level concurrency? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 109
You have a GitHub repository.
You use the GitHub Copilot CLI to run an agentic workflow from the terminal.
During execution, the conversation history approaches the context limit. The agent cannot continue the current session unless the amount of retained context is reduced.
You need to continue the current session without losing all prior progress.
Which Copilot CLI slash command should you run?
正解：C
解説：
The /compact command reduces the active conversation context while preserving a condensed summary of the important work completed so far. It is designed for long-running agent sessions that approach the context-window limit.
/clear would remove the session context rather than preserve the useful implementation history. This creates a sharper loss of continuity and does not meet the requirement to retain prior progress. /yolo concerns approval behavior, not context reduction. /context is used to inspect or manage context information but does not perform the required compaction.
Compaction is most effective when durable project state already exists in repository artifacts: source changes, commits, task files, plans, and test outputs. The summarized context can then retain decisions and current goals while the repository continues to hold the detailed implementation record.
Study-guide topics: context compaction, long-running sessions, durable state, and execution continuity.
質問 # 110
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?
正解：D
解説：
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:
質問 # 111
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GH-600試験番号: https://www.jptestking.com/GH-600-exam.html