GH-600資格トレーニング、GH-600受験記

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Manage memory, state, and execution	10–15%	- Persist agent state and manage context drift 1. Resume agent work without repeating steps or diverging from prior decisions 2. Detect and correct drift during extended agent execution 3. Capture task progress and decisions as durable artifacts - Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Prevent conflicting context 3. Share agent state - 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
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. Specify expected outcomes and operational constraints for agent tasks 3. Generate evaluation signals by using automated scanning tools 4. Align evaluation criteria with development intent - 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 - Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Refine memory usage 3. Revise instructions, workflows, or constraints
Orchestrate multi-agent coordination	15–20%	- Manage the lifecycle of agents within multi-agent workflows 1. Add agents to existing multi-agent workflows 2. Retire agents while preserving auditability and workflow continuity 3. Update, reconfigure, or replace agents without disrupting active workflows - 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 - 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. Perform post-hoc analysis of multi-agent behavior 3. Document key decisions, handoffs, and outcomes across agents - Operate and manage multi-agent workflows 1. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 2. Configure agent isolation for parallel execution 3. Apply an orchestration pattern to coordinate multiple agents
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 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 - Define boundaries between planning, reasoning, and action 1. Prevent agent action until the agent checks and approves 2. Validate agent plans 3. Configure agent planning to be distinct from agent execution 4. Configure an agent to output a structured plan
Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Scope permissions and execution contexts to enforce least-privilege access 2. Block actions that violate defined security, compliance, or Responsible AI policies 3. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 4. Preserve execution velocity by minimizing approvals that do not materially reduce risk 5. Identify the subset of actions that require human judgment - 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 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 - Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Configure an agent to be invoked in a CI workflow 3. Configure an agent to handle environment-specific constraints 4. Enable an agent to perform autonomous actions, including creating branches and pull requests 5. Configure an agent to use branch-based scope 6. Evaluate the execution context for an agent - Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement traceability and accountability for agent actions 3. Implement escalation paths 4. Implement rollbacks 5. Implement error handling - Configure MCP servers 1. Configure a GitHub remote MCP server 2. Add an MCP server as a tool to an agent 3. Configure MCP registries 4. Configure MCP allow lists

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GH-600受験記 & GH-600日本語受験教科書

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Microsoft Developing in Agentic AI Systems 認定 GH-600 試験問題 (Q42-Q47):

質問 # 42
A designated top-level instructions file is used by some agentic tools to describe overall repository purpose, build/test commands, and conventions in a tool-agnostic way (usable across multiple AI coding agents, not just Copilot). What is this file commonly called?

A. agents.md
B. .copilotignore
C. copilot-instructions.md
D. copilot-setup-steps.yml

正解：A

解説：
agents.md (or AGENTS.md) is an emerging convention for providing agent-agnostic project context - build steps, structure, conventions -- that multiple AI coding tools (not just Copilot) can consume.

質問 # 43
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?

A. /compact
B. /context
C. /clear
D. /yolo

正解：A

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 # 44
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 # 45
You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?

A. Add clear, reproducible steps and expected behavior to the issue
B. Increase MCP server rate limits
C. Enable Copilot memory
D. Switch the agent to --allow-all mode

正解：A

解説：
Agents rely entirely on the context provided in the issue. Vague issues lead to incorrect fixes.
Providing reproduction steps, error logs, and expected behavior gives the agent the information it needs to correctly diagnose and resolve the root cause.

質問 # 46
You want a reusable, shareable set of instructions that defines a specific persona/workflow (e.g.,
"security reviewer") that developers can invoke on demand in Copilot Chat, rather than instructions applied globally to every session. What should you create?

A. agents.md
B. A prompt file (.prompt.md)
C. .copilotignore
D. copilot-instructions.md

正解：B

解説：
Prompt files define reusable, invocable prompt templates for specific tasks or personas. Unlike copilot-instructions.md, which applies globally and automatically, prompt files are opt-in and triggered explicitly.

質問 # 47
......

もし君がMicrosoftのGH-600に参加すれば、良い学習のツルを選ぶすべきです。MicrosoftのGH-600認定試験はIT業界の中でとても重要な認証試験で、合格するために良い訓練方法で準備をしなければなりません。。

GH-600受験記: https://www.passtest.jp/Microsoft/GH-600-shiken.html