GH-600試験の準備方法｜素晴らしいGH-600日本語対策試験｜素敵なDeveloping in Agentic AI Systems無料過去問

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement tool use and environment interaction	20–25%	- Configure MCP servers 1. Configure MCP allow lists 2. Add an MCP server as a tool to an agent 3. Configure MCP registries 4. Configure a GitHub remote MCP server - Select and configure agent tools 1. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions - Integrate agents within development environments 1. Configure an agent to handle environment-specific constraints 2. Configure an agent to be invoked in a CI workflow 3. Evaluate the execution context for an agent 4. Configure an agent's scope to a specific repository 5. Configure an agent to use branch-based scope 6. Enable an agent to perform autonomous actions, including creating branches and pull requests - Operate agents with safe execution paths and robust error handling 1. Implement escalation paths 2. Implement retries 3. Implement traceability and accountability for agent actions 4. Implement rollbacks 5. Implement error handling
Topic 2: Manage memory, state, and execution	10–15%	- Ensure continuity of agent memory and state across tools and environments 1. Share agent state 2. Prevent stale context 3. Prevent conflicting context - Implement agent memory strategies 1. Scope agent memory to task-relevant information 2. Choose between short-term, long-term, and external memory 3. Define memory expiration, pruning, and reset rules - Persist agent state and manage context drift 1. Capture task progress and decisions as durable artifacts 2. Detect and correct drift during extended agent execution 3. Resume agent work without repeating steps or diverging from prior decisions
Topic 3: Prepare agent architecture and SDLC processes	15–20%	- 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 agents to produce inspectable artifacts within standard development tooling 2. Configure human intervention for autonomous agents without slowing delivery 3. Plan and implement the degree of agent autonomy, including guardrails - Define boundaries between planning, reasoning, and action 1. Prevent agent action until the agent checks and approves 2. Configure agent planning to be distinct from agent execution 3. Validate agent plans 4. Configure an agent to output a structured plan
Topic 4: Perform evaluation, error analysis, and tuning	15–20%	- Define success criteria and evaluation signals for agent tasks 1. Identify qualitative and quantitative evaluation signals to evaluate agents 2. Generate evaluation signals by using automated scanning tools 3. Align evaluation criteria with development intent 4. Specify expected outcomes and operational constraints for agent tasks - Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Revise instructions, workflows, or constraints 3. Refine memory usage - 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
Topic 5: Orchestrate multi-agent coordination	15–20%	- 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 - 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. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 2. Apply an orchestration pattern to coordinate multiple agents 3. Configure agent isolation for parallel execution - Detect and respond to multi-agent failures and degraded behavior 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 2. Identify failed, partial, or stalled agent executions 3. Respond to degraded behavior or coordination across agents
Topic 6: Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 2. Identify the subset of actions that require human judgment 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. Block actions that violate defined security, compliance, or Responsible AI policies - Define autonomy levels 1. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards 2. Classify agent actions by operational, security, and compliance risk to right-size human interventions

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Microsoft Developing in Agentic AI Systems 認定 GH-600 試験問題 (Q51-Q56):

質問 # 51
A team assigns an issue to the GitHub Copilot coding agent by using the following one-line description: Fix the login bug.
Copilot creates a pull request, but the pull request is missing changes and has an incorrect scope.
How should you resolve the issue?

A. Enable Copilot memory.
B. Add a clear description of the problem to the issue.
C. Update the Model Context Protocol (MCP) server rate limits.
D. Allocate more resources to copilot-setup-steps.yml.

正解：B

解説：
The primary failure is insufficient task definition. "Fix the login bug" does not identify the observed behavior, expected behavior, reproduction conditions, affected component, or completion criteria. The agent must infer these details, creating a substantial risk of incomplete changes or work outside the intended scope. A clear issue description supplies the information needed to construct and implement an appropriate plan.
For this scenario, the description should identify how the login failure occurs, which authentication path is affected, what successful behavior looks like, and which existing behavior must remain intact. Relevant error messages, reproduction steps, and expected tests make the task independently verifiable. These details turn an ambiguous request into a bounded engineering assignment.
Enabling memory does not supply missing requirements reliably. MCP rate limits govern service interaction rather than task clarity. Additional setup resources address environmental capacity or dependency preparation, not uncertainty about what must change.
The corrective action therefore belongs at task intake, where developers define the agent's inputs and success conditions. The relevant curriculum topics are defining agent inputs, outputs, and success criteria and mitigating poorly scoped task assignments.
Reference:

質問 # 52
You are evaluating the logs of the multi-agent workflow in repo2.
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 # 53
You have a repository that uses a GitHub Actions workflow to run an agent-driven change plan as part of a CI pipeline. The workflow generates an artifact named plan.json that includes a field named risk. Risk has possible values of low, medium, or high.
You need to ensure that a human must confirm the execution of the workflow when risk is medium or high.
The workflow must proceed automatically only when risk is low.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

正解：

解説：

Explanation:
Target
Value
approval_required.if
${{ needs.plan.outputs.risk != ' low ' }}
apply_auto.if
${{ needs.plan.outputs.risk == ' low ' }}
The approval job must run whenever the computed risk is not low. The expression ${{ needs.plan.outputs.risk
!= ' low ' }} includes both medium and high risk values, routing those plans through the protected approval environment.
The automatic application job must run only when the plan output explicitly equals low. The expression ${{ needs.plan.outputs.risk == ' low ' }} prevents the automatic path from running for medium, high, missing, or unexpected risk values.
These conditions should be evaluated from a defined output of the preceding plan job. The plan job must therefore parse plan.json, validate the risk value, and expose it through jobs. < job_id > .outputs. A malformed, empty, or unknown risk value should not fall through to automatic execution.
The environment gate supplies the human confirmation mechanism. The conditional logic only routes jobs; it does not itself create approval requirements.
Study-guide topics: risk-based routing, conditional workflow execution, protected environments, and agent autonomy controls.

質問 # 54
A developer uses the GitHub Copilot CLI in plan mode.
Copilot produces a plan.
What does Copilot do next?

A. saves the plan to plan.md
B. creates a branch named /copilot/plan in the repository
C. begins the implementation
D. opens a pull request that has the plan as a comment

正解：A

解説：
In plan mode, after GitHub Copilot CLI analyzes your codebase and gathers requirements, it saves the plan to plan.md in your session folder.
What Happens Next?
1. Saves to plan.md: It writes the structured implementation plan with checkable tasks into a file called plan.md.
2. Waits for Approval: Copilot pauses and waits for your review. It will not begin implementation or make code modifications until you explicitly approve the strategy.
3. Execution: Once approved, you can hand the plan off to Copilot's agent/autopilot mode to begin the implementation.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-cli/cli-best-practices

質問 # 55
You have a GitHub repository that uses the GitHub Copilot coding agent to run agent plans for assigned issues.
You discover that some issues contain ambiguous implementation steps.
You need to reduce the likelihood that Copilot executes plans based on ambiguous implementation steps.
What should you do?

A. Mention @copilot in a pull request comment to request changes after the plan runs.
B. Add comments to the issues after assigning the issues to the agent.
C. Add acceptance criteria and directions to the issues before assigning the issues to the agent.
D. Add a copilot-setup-steps.yml file to preinstall dependencies in the agent environment.

正解：C

解説：
The most effective intervention occurs before the issue becomes an executable assignment. Acceptance criteria define the observable conditions that constitute success, while implementation directions establish boundaries and clarify how the work should proceed. Providing both before assignment reduces the need for the agent to infer missing requirements during planning.
Applied to this scenario, the issue should distinguish required outcomes from optional improvements and identify constraints that the implementation must respect. Where steps are ambiguous, the author should resolve the ambiguity or explicitly require clarification before execution. Reviewable criteria also give the agent and maintainers a shared basis for evaluating whether the resulting changes satisfy the original request.
Options A and D intervene after assignment or execution has begun. They may support later correction, but they do not prevent the initial plan from being based on unclear directions. Option C addresses dependency availability, which is a separate environmental concern.
The governing design principle is to make the task sufficiently definite before delegating action. Relevant curriculum topics include defining inputs and success criteria, validating agent plans, and separating preparation from execution.
References: GH-600 study guide , Responsible use of Copilot agents .

質問 # 56
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これらの有用な知識をよりよく取り入れるために、多くの顧客は、実践する価値のある種類の練習資料を持ちたいと考えています。すべてのコンテンツは明確で、GH-600実践資料で簡単に理解できます。リーズナブルな価格とオプションのさまざまなバージョンでアクセスできます。すべてのコンテンツは試験の規制に準拠しています。成功することが決まっている限り、GH-600学習ガイドがあなたの最善の信頼になります

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