GH-600関連資料、GH-600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%	- Select and configure agent tools 1. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions - Configure MCP servers 1. Configure a GitHub remote MCP server 2. Configure MCP allow lists 3. Add an MCP server as a tool to an agent 4. Configure the MCP registries - Integrate agents within development environments 1. Configure an agent to use branch-based scope 2. Enable an agent to perform autonomous actions, including creating branches and pull requests 3. Configure an agent to be invoked in a CI workflow 4. Evaluate the execution context for an agent 5. Configure an agent's scope to a specific repository 6. Configure an agent to handle environment-specific constraints - Operate agents with safe execution paths and robust error handling 1. Implement traceability and accountability for agent actions 2. Implement error handling 3. Implement retries 4. Implement escalation paths 5. Implement rollbacks
Topic 2: Prepare agent architecture and SDLC processes	15-20%	- Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Identify steps for agents to perform 3. Define inputs, outputs, and success criteria for agents - Configure observability and control for autonomous agents 1. Configure agents to produce inspectable artifacts within standard development tooling 2. Plan and implement the degree of agent autonomy, including guardrails 3. Configure human intervention for autonomous agents without slowing delivery - Define boundaries between planning, reasoning, and action 1. Configure agent planning to be distinct from agent execution 2. Validate agent plans 3. Prevent agent action until the agent checked and approved 4. Configure an agent to output a structured plan
Topic 3: Perform evaluation, error analysis, and tuning	15-20%	- Define success criteria and evaluation signals for agent tasks 1. Specify expected outcomes and operational constraints for agent tasks 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Generate evaluation signals by using automated scanning tools 4. Align evaluation criteria with development intent - Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Refine memory usage 3. Revise instructions, workflows, or constraints - 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
Topic 4: Manage memory, state, and execution	10-15%	- Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Share agent state 3. Prevent conflicting context - Persist agent state and manage context drift 1. Capture task progress and decisions as durable artifacts 2. Resume agent work without repeating steps or diverging from prior decisions 3. Detect and correct drift during extended agent execution - Implement agent memory strategies 1. Choose between short-term, long-term, and external memory 2. Define memory expiration, pruning, and reset rules 3. Scope agent memory to task-relevant information
Topic 5: Implement guardrails and accountability	10-15%	- 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 - Implement guardrails and human-in-the-loop workflows 1. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 2. Scope permissions and execution contexts to enforce least-privilege access 3. Identify the subset of actions that require human judgment 4. Block actions that violate defined security, compliance, or Responsible AI policies 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk
Topic 6: 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. Add agents to existing multi-agent workflows 3. Retire agents while preserving auditability and workflow continuity - Operate and manage multi-agent workflows 1. Apply an orchestration pattern to coordinate multiple agents 2. Configure agent isolation for parallel execution 3. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs - 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 - Detect and respond to multi-agent failures and degraded behavior 1. Identify failed, partial, or stalled agent executions 2. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 3. Respond to degraded behavior or coordination across agents

>> GH-600関連資料 <<

試験の準備方法-最新のGH-600関連資料試験-真実的なGH-600過去問

参考のためにいくつかの利点を提供しています。一方では、GH-600学習の質問により、作業スタッフが顧客の多様で進化する期待を理解し、その理解を戦略に取り入れることで、GH-600試験エンジンを100％信頼できます。一方、プロのGH-600学習資料が高い合格率を決定します。調査統計によると、当社製品を使用した後の99％の候補者がGH-600試験に合格したことを自信を持って伝えることができます。

Microsoft Developing in Agentic AI Systems 認定 GH-600 試験問題 (Q55-Q60):

質問 # 55
You need to implement the security requirements for agent1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

正解：

解説：

Explanation:
Order
Action
1
In the product-api repository settings, create a GitHub Actions environment named plan-review and add SG_Review as the required reviewers.
2
To the agent1 workflow, add a plan job that runs first and uploads the plan output as a workflow artifact.
3
Add an execute job that has needs: [plan] and environment: plan-review.
The review environment must exist before a workflow job can reference it. Required reviewers on plan- review establish the approval gate that protects execution of the agent's proposed plan.
The workflow then requires a plan job that runs before execution and uploads the plan as an artifact. This creates a durable review object rather than requiring reviewers to approve an undocumented or transient agent decision. The artifact is available for inspection during the approval stage and for later audit.
Finally, the execute job declares needs: [plan] to enforce sequencing and targets the plan-review environment.
When that job reaches the protected environment, required reviewers must approve it before the protected execution can proceed. This creates a controlled sequence: generate evidence, review evidence, then authorize execution.
Branch-protection rules and automatic advancement are not substitutes for environment-based approval of the execution job.
Study-guide topics: human-in-the-loop approvals, protected environments, artifacts, and staged agent execution.

質問 # 56
You have a GitHub repository that uses GitHub Copilot code review on pull requests.
You plan to add repository-wide code review guidance that will apply to all files.
You need Copilot code review to consistently apply the guidance during pull request reviews.
What should you do?

A. Create a .github/copilot-instructions.md file.
B. Modify the .github/pull_request_template.md file.
C. Update the instructions in the .github/agents/*.agent.md files.
D. Create a .github/instructions/copilot.instructions.md file.

正解：A

解説：
The repository-wide Copilot instruction file is .github/copilot-instructions.md. GitHub explicitly identifies this file as the location for review guidance that should apply throughout the codebase. It can describe coding standards, security expectations, error-handling requirements, and other review criteria that should be considered across pull requests.
A pull request template primarily structures the description supplied when a pull request is created. It is not the designated repository-wide Copilot instruction mechanism. Files beneath .github/instructions support instructions with defined applicability, commonly using path patterns. The filename in option C alone does not establish repository-wide scope. Custom agent profiles define the behavior of particular agents and do not replace the standard configuration for Copilot code review.
The instructions should state concrete, reviewable requirements rather than vague preferences. For example, a rule about checking authorization at a defined service boundary is more actionable than a general instruction to "ensure security." Repository settings must also allow custom instructions to be used for code review.
This configuration improves consistency while human reviewers remain responsible for evaluating the resulting findings.
Relevant curriculum topics are tuning instructions, defining evaluation criteria, and aligning automated review with development intent.
References: About GitHub Copilot code review , Adding repository custom instructions .

質問 # 57
You have the following agent logs.

正解：

解説：

Explanation:
Statement
Answer
The agent responded with messages.
Yes
The agent can edit files in the repository.
No
The agent analyzed files in the repository.
Yes
The log contains successful message-return events, confirming that the agent responded. It also shows successful model activity associated with the agent session, which supports the conclusion that the agent processed available information.
The log does not provide evidence of an actual file-edit tool invocation or successful repository write. An editAgent panel label identifies the interaction context; it is not equivalent to proof that the agent had or used file-edit permissions. Therefore, the correct response for file-edit capability is No.
The model responses and session activity demonstrate analysis, but they do not establish every possible capability of the agent. Effective log analysis distinguishes between evidence of observed actions and assumptions based on an agent's name, panel, or intended role.
Study-guide topics: log interpretation, evidence-based evaluation, tool-call analysis, and capability verification.

質問 # 58
You are evaluating the following workflow YAML.
jobs:
agent1:
runs-on: ubuntu-latest
environment: agent-sandbox
steps:
- uses: actions/checkout@v4
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 # 59
You are architecting an agentic AI system and need the agent's tool-calling behavior to be constrained so it can only call a specific allow-listed set of MCP tools, never arbitrary ones. What should you configure?

A. Tool/server allow-list in the MCP client configuration
B. /usage
C. .copilotignore
D. Repository ruleset

正解：A

解説：
The correct answer is A because tool availability must be constrained at the agent/MCP integration boundary. GitHub custom-agent configuration provides a tools property that explicitly determines which tools the agent is permitted to use, including tools supplied by configured MCP servers. Rather than enabling all tools with ["*"] or omitting the restriction, administrators can specify individual tool names or aliases and thereby implement an explicit allow-list.
This is a fundamental least-privilege control for agentic systems. An MCP server can expose numerous capabilities, but exposure by the server does not mean every capability should automatically be available to every agent. Restricting the agent to the tools required for its assigned role reduces unintended actions and limits the blast radius of erroneous reasoning, prompt injection, or excessive autonomy. GitHub also supports repository MCP configuration through which administrators determine which external MCP integrations are available.
.copilotignore does not implement MCP authorization. Repository rulesets govern GitHub repository operations such as branches and merges, while /usage reports runtime/session information.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction; MCP integration; tool allow-listing; least privilege; execution boundaries.

質問 # 60
......

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