GH-600認証試験、GH-600日本語サンプル

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Manage memory, state, and execution	10–15%	- Implement agent memory strategies 1. Define memory expiration, pruning, and reset rules 2. Scope agent memory to task-relevant information 3. Choose between short-term, long-term, and external memory - 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 - Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Share agent state 3. Prevent conflicting context
Implement tool use and environment interaction	20–25%	- Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement retries 3. Implement traceability and accountability for agent actions 4. Implement rollbacks 5. Implement escalation paths - Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure MCP allow lists 3. Configure a GitHub remote MCP server 4. Configure MCP registries - Integrate agents within development environments 1. Enable an agent to perform autonomous actions, including creating branches and pull requests 2. Evaluate the execution context for an agent 3. Configure an agent's scope to a specific repository 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. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions
Perform evaluation, error analysis, and tuning	15–20%	- Define success criteria and evaluation signals for agent tasks 1. Generate evaluation signals by using automated scanning tools 2. Specify expected outcomes and operational constraints for agent tasks 3. Identify qualitative and quantitative evaluation signals to evaluate agents 4. Align evaluation criteria with development intent - Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine memory usage 3. Refine tool usage and tool access - Analyze agent failures and identify root causes 1. Classify root causes, including reasoning errors, tool misuse, and context or environment issues 2. Identify failures by using logs, plans, traces, outputs, and workflow artifacts
Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Identify the subset of actions that require human judgment 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. Block actions that violate defined security, compliance, or Responsible AI policies - 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
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. Respond to degraded behavior or coordination across agents 3. Implement multi-agent recovery patterns, including rollback and human-in-the-loop - Manage the lifecycle of agents within multi-agent workflows 1. Update, reconfigure, or replace agents without disrupting active workflows 2. Add agents to existing multi-agent workflows 3. Retire agents while preserving auditability and workflow continuity - 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 - 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
Prepare agent architecture and SDLC processes	15–20%	- Define boundaries between planning, reasoning, and action 1. Configure agent planning to be distinct from agent execution 2. Validate agent plans 3. Configure an agent to output a structured plan 4. Prevent agent action until the agent checks and approves - Integrate agents into the software development lifecycle (SDLC) 1. Identify steps for agents to perform 2. Define inputs, outputs, and success criteria for agents 3. Identify and mitigate common anti-patterns in agents - 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

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GH-600日本語サンプル、GH-600試験対策

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Microsoft Developing in Agentic AI Systems 認定 GH-600 試験問題 (Q68-Q73):

質問 # 68
You have a GitHub Copilot Enterprise subscription. Developers use Microsoft Visual Studio Code and GitHub Copilot.
The developers want to share a custom agent that can review code changes but cannot modify files.
You need to prevent the agent from modifying repository content and appearing in the agents dropdown.
How should you configure the YAML frontmatter for the agent? 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.

正解：

解説：

質問 # 69
After App1 is upgraded to meet the technical requirements, you need to validate the output.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

正解：

解説：

質問 # 70
After several long agentic sessions, you notice degraded response quality and want the agent to retain a concise summary of prior progress rather than the full raw history, while continuing without interruption. Which approach addresses this?

A. --allow-all
B. /compact
C. /clear
D. .copilotignore

正解：B

解説：
/compact summarizes the existing conversation history via the model and replaces the raw history with the condensed summary, preserving continuity and prior progress while freeing up context window space -- unlike /clear, which discards everything.

質問 # 71
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.
You need to make changes to repo1 to support the planned changes for the agents.
What should you modify?

A. .vscode/mcp.json
B. .vscode/settings.json
C. <project>/.mcp/server.json
D. .github/agents/*.agent.md

正解：A

解説：
Scenario, Planned Changes: Contoso plans to have all agents and developers in repo1 use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
To centrally configure the Microsoft Learn Model Context Protocol (MCP) server for all developers and agents within a shared repository, you should modify the .vscode/mcp.json file.
Central Repository Configuration: In GitHub Copilot and Visual Studio Code, placing an mcp.json file inside the workspace root's .vscode/ directory ensures that the defined MCP servers are automatically loaded and shared with any developer or Copilot agent who opens that specific project repository.
Tool Exposure: This configuration file maps the external tools provided by the Microsoft Learn Docs MCP server (such as searching and fetching official documentation) directly into the Copilot agentic workflow.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers

質問 # 72
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent.
Engineers assign issues to Copilot, and Copilot creates draft pull requests. The engineers start tasks either by assigning issues on github.com or by using GitHub Copilot Chat in an IDE.
Reviewers request updates by leaving pull request comments.
You discover that sometimes, Copilot uses outdated requirements after a reviewer posts an updated instruction in a pull request comment, and, in several cases, Copilot fails to resume work from the comment.
You need to ensure that iteration requests are applied to the correct pull request session and are processed consistently.
What should you do?

A. Start a new task from Copilot Chat in the IDE. Instruct the reviewer to mention @github in the task comments.
B. Ensure that the agent has write access to the repository. Instruct the reviewer to mention @github in the task comments.
C. Ensure that the reviewer has write access to the repository. Instruct the reviewer to mention
@copilot in the pull request comments.
D. Ensure that the agent has write access to the repository. Instruct the reviewer to unassign and reassign the original issue to Copilot.

正解：C

解説：
You should explicitly instruct the reviewer to mention @copilot in the pull request comments, and you must verify that they have write access to the repository.
Explicit Triggering: GitHub Copilot's asynchronous coding agent listens for active summons in existing pull request threads. Simply leaving a comment without an explicit @copilot mention may cause it to skip processing the update entirely, explaining why it fails to resume work.
Context Preservation: By directly mentioning @copilot within the specific pull request comment thread, the agent binds the new instructions directly to that exact active development session.
This prevents it from pulling outdated requirements from the initial issue definition.
Permission Enforcement: GitHub Copilot will ignore commands and mentions from users who do not possess write permissions (or higher) on the repository to prevent unauthorized code modifications or resource usage.
Reference:
https://github.blog/ai-and-ml/github-copilot/assigning-and-completing-issues-with-coding-agent-in-github-copilot/

質問 # 73
......

今日、MicrosoftのGH-600認定試験は、IT業界で多くの人に重視されています、それは、IT能力のある人の重要な基準の目安となっています。多くの人はMicrosoftのGH-600試験への準備に悩んでいます。この記事を読んだあなたはラッキーだと思います。あなたは最高の方法を探しましたから。私たちの強力なJpexamチームの開発するMicrosoftのGH-600ソフトを使用して試験に保障があります。まだ躊躇？最初に私たちのソフトウェアのデモを無料でダウンロードしよう。

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