GH-600テスト対策書 & GH-600学習教材

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Perform evaluation, error analysis, and tuning	15-20%	- 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 - Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine memory usage 3. Refine tool usage and tool access - Define success criteria and evaluation signals for agent tasks 1. Align evaluation criteria with development intent 2. Specify expected outcomes and operational constraints for agent tasks 3. Identify qualitative and quantitative evaluation signals to evaluate agents 4. Generate evaluation signals by using automated scanning tools
Prepare agent architecture and SDLC processes	15-20%	- 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. Configure an agent to output a structured plan 2. Validate agent plans 3. Configure agent planning to be distinct from agent execution 4. Prevent agent action until the agent checked and approved - 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
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. 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. 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
Orchestrate multi-agent coordination	15-20%	- 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 - Detect and respond to multi-agent failures and degraded behavior 1. Respond to degraded behavior or coordination across agents 2. Identify failed, partial, or stalled agent executions 3. Implement multi-agent recovery patterns, including rollback and human-in-the-loop - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Configure multi-agent workflows to produce artifacts suitable for review and audit 2. Document key decisions, handoffs, and outcomes across agents 3. Perform post-hoc analysis of multi-agent behavior - Manage the lifecycle of agents within multi-agent workflows 1. Retire agents while preserving auditability and workflow continuity 2. Add agents to existing multi-agent workflows 3. Update, reconfigure, or replace agents without disrupting active workflows
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. Block actions that violate defined security, compliance, or Responsible AI policies 4. Preserve execution velocity by minimizing approvals that do not materially reduce risk 5. Scope permissions and execution contexts to enforce least-privilege access - 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
Implement tool use and environment interaction	20-25%	- Integrate agents within development environments 1. Configure an agent to be invoked in a CI workflow 2. Configure an agent to handle environment-specific constraints 3. Evaluate the execution context for an agent 4. Enable an agent to perform autonomous actions, including creating branches and pull requests 5. Configure an agent's scope to a specific repository 6. Configure an agent to use branch-based scope - Configure MCP servers 1. Configure MCP allow lists 2. Configure a GitHub remote MCP server 3. Add an MCP server as a tool to an agent 4. Configure the MCP registries - Operate agents with safe execution paths and robust error handling 1. Implement traceability and accountability for agent actions 2. Implement escalation paths 3. Implement error handling 4. Implement retries 5. Implement rollbacks - Select and configure agent tools 1. Configure agent tool permissions 2. Configure agent tools 3. Identify required tools

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GH-600学習教材、GH-600復習範囲

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Microsoft Developing in Agentic AI Systems 認定 GH-600 試験問題 (Q95-Q100):

質問 # 95
You have a GitHub repository that uses GitHub Actions for CI.
Your team is piloting the GitHub Copilot coding agent to autonomously create branches and open pull requests. The repository follows trunk-based development that uses main as the default branch.
You need to ensure that the agent meets the following requirements:
Changes to main can occur only by using pull requests that have at least one approval.
When a pull request is opened, a validation workflow runs.
How should you configure the repository? To answer, select the appropriate options in the answer area.

正解：

解説：

質問 # 96
You have a GitHub Enterprise organization that has Copilot memory enabled.
You create a new repository.
What are two ways that memories will be deleted from the repository? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. when the repository is archived
B. when pull requests are merged
C. manually by repository members
D. when the code that created the memory is deleted
E. automatically after 28 days

正解：C、E

解説：
GitHub Copilot repository memories can be removed manually and through automatic retention cleanup. Repository owners or administrators can review repository-level facts under the repository's Copilot Memory settings and delete stored facts that are obsolete, misleading, or no longer appropriate. This makes A a valid deletion mechanism.
D is also correct. GitHub applies an inactivity-based retention policy: a stored repository fact or user preference that goes unused for 28 days is automatically deleted. The 28-day period can restart when Copilot successfully validates and uses that memory, so this should be understood as an unused-memory expiration period rather than an unconditional deletion exactly 28 days after creation.
Deleting the source code referenced by a memory does not directly delete that memory. Repository-level facts retain citations and are validated against the current branch before use; if the supporting information is no longer valid, Copilot does not use the fact. Likewise, archiving a repository or merging a pull request is not documented as an automatic memory-deletion event.
Study Guide Reference Topics: Manage Memory, State, and Execution; repository memory lifecycle; memory validation; memory retention and deletion; persistent agent knowledge.

質問 # 97
Hotspot Question
You have a GitHub repository that uses GitHub Actions for CI.
Your team is piloting the GitHub Copilot coding agent to autonomously create branches and open pull requests. The repository follows trunk-based development that uses main as the default branch.
You need to ensure that the agent meets the following requirements:
- Changes to main can occur only by using pull requests that have at
least one approval.
- When a pull request is opened, a validation workflow runs, and the
agent can still create branches and open pull requests autonomously.
How should you configure the repository? 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: Enabled
To meet the requirements, the Branch protection rule (main) - Require pull request before merging must be Enabled. This forces everyone-including GitHub Actions and AI agents-to create a branch and open a pull request first.
Box 2: on: pull_request
To allow your GitHub Copilot agent to autonomously open PRs while maintaining branch protection, you must trigger your validation workflow on the pull_request event.
Bypasses Protection: Agents cannot push directly to main due to your branch protection rule.
They must create a feature branch and open a PR.Triggers on Creation: The pull_request trigger fires automatically the moment the agent opens a PR.Runs in Isolation: It validates the merged result of the agent's branch and main before any actual code is merged.
Reference:
https://tutorialsdojo.com/github-copilot-coding-agent/

質問 # 98
You need to enable Copilot memory to support the planned changes.
What should you configure?

A. The agent profile of agent1
B. The Copilot settings of the organization
C. The Copilot settings of each repository
D. The personal Copilot settings of each developer

正解：B

質問 # 99
Drag and Drop Question
You have a GitHub repository that uses a custom GitHub Copilot coding agent defined in the rollout-bot.agent.md file.
You need to update a workflow so that agent-profile changes can be rolled back by reverting a single commit and rerunning the workflow. The workflow must check out the exact commit being deployed and apply the agent profile from the repository at that commit.
How should you complete the workflow? 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：

Explanation:
Box 1: inputs.ref
The specific commit being targeted.
Box 2: agents/rollout-bot.agent.md
The profile file to validate
Box 3: ${{ github.sha }}
The exact commit ref that the checkout action fetched
Reference:
https://octopus.com/devops/github-actions/github-actions-workflows/

質問 # 100
......

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GH-600学習教材: https://www.xhs1991.com/GH-600.html