GH-600コンポーネント & GH-600試験勉強過去問

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Orchestrate multi-agent coordination	15–20%	- Operate and manage multi-agent workflows 1. Apply an orchestration pattern to coordinate multiple agents 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 3. Configure agent isolation for parallel execution - 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. Configure multi-agent workflows to produce artifacts suitable for review and audit 3. Document key decisions, handoffs, and outcomes across agents - Detect and respond to multi-agent failures and degraded behavior 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 2. Respond to degraded behavior or coordination across agents 3. Identify failed, partial, or stalled agent executions
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. Define inputs, outputs, and success criteria for agents 3. Identify steps for agents to perform - Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Configure agent planning to be distinct from agent execution 3. Configure an agent to output a structured plan 4. Prevent agent action until the agent checks and approves - Configure observability and control for autonomous agents 1. Plan and implement the degree of agent autonomy, including guardrails 2. Configure human intervention for autonomous agents without slowing delivery 3. Configure agents to produce inspectable artifacts within standard development tooling
Topic 3: Implement tool use and environment interaction	20–25%	- Configure MCP servers 1. Configure a GitHub remote MCP server 2. Configure MCP registries 3. Configure MCP allow lists 4. Add an MCP server as a tool to an agent - Operate agents with safe execution paths and robust error handling 1. Implement traceability and accountability for agent actions 2. Implement retries 3. Implement error handling 4. Implement escalation paths 5. Implement rollbacks - Select and configure agent tools 1. Configure agent tool permissions 2. Configure agent tools 3. Identify required tools - 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. Evaluate the execution context for an agent 4. Configure an agent to handle environment-specific constraints 5. Configure an agent to be invoked in a CI workflow 6. Configure an agent's scope to a specific repository
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. Prevent conflicting context 3. Share agent state - 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 - 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
Topic 5: Implement guardrails and accountability	10–15%	- 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 guardrails and human-in-the-loop workflows 1. Scope permissions and execution contexts to enforce least-privilege access 2. Identify the subset of actions that require human judgment 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. Block actions that violate defined security, compliance, or Responsible AI policies
Topic 6: Perform evaluation, error analysis, and tuning	15–20%	- 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. 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 - Define success criteria and evaluation signals for agent tasks 1. Generate evaluation signals by using automated scanning tools 2. Align evaluation criteria with development intent 3. Specify expected outcomes and operational constraints for agent tasks 4. Identify qualitative and quantitative evaluation signals to evaluate agents

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GH-600試験勉強過去問、GH-600対応内容

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Microsoft Developing in Agentic AI Systems 認定 GH-600 試験問題 (Q46-Q51):

質問 # 46
You have a GitHub Enterprise Cloud repository that uses GitHub Actions for CI and requires pull requests for all changes.
You are planning a GitHub Actions workflow where a coding agent drafts implementation changes and tests from GitHub issues, and an automated review runs before human review.
You need the agent to draft changes from an assigned issue, open a pull request, and add an automated review to the pull request before requesting a human review.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

質問 # 47
Hotspot Question
You have the following agent logs.

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：

Explanation:
Box 1: Yes
Yes, the agent did respond with messages.
Markdown Message: The log ccreq:XXX.copilotmd | markdown message 0 returned: finish reason: [stop] indicates that a text-based markdown response was successfully generated and completed.
Language Model Output: The log ccreq:XXX.copilotmd shows the core language model (gpt-4o- mini) completed its request, delivering the main content of the message.
Box 2: Yes
Based on the log entries provided, yes, the agent can edit files in the repository.
Evidence from the LogsTargeted Component: The final three log entries explicitly target the component [panel/editAgent].
Model Selection: For these specific tasks, the system switches from standard conversational models (gpt-4o-mini) to a specialized code-generation model: gpt-5.3-codex.
Processing Time: These operations take significantly longer than standard chat generation, which is characteristic of complex code-parsing and file-modification tasks.
Box 3: Yes
Based on the log entries provided, yes, the agent analyzed files in the repository.
Action Type: The final three entries explicitly call the [panel/editAgent] capability.
Model Used: These actions utilized the gpt-5.3-codex model, which is specialized for codebases.
Operation Duration: The execution times were significantly longer.
Implied Task: Code-generation and editing agents require repository file analysis to perform edits.
Reference:
https://learn.microsoft.com/en-us/troubleshoot/power-platform/copilot-studio/authoring/error-codes

質問 # 48
Case Study 2
Existing Environment
GitHub Environment
The GitHub environment contains the following:
- Three repositories named product-api, billing-service, and infra-terraform.
- Branch protection on the main branch in all repositories that requires at least one pull request review before merging
- GitHub Actions runners used across all workflows
- A GitHub team named SG_Dev that contains developers
- A GitHub team named SG_Review that contains senior engineers and a security team
- A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
- No custom agent profile is defined.
- A Model Context Protocol (MCP) server named MCP1 is deployed to
https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
MCP1 requires an API key for authentication.
A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
Copilot memory is NOT enabled for the organization.
Problem Statements
Litware identifies the following issues:
- During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope.
- agent1 makes code changes immediately after receiving a task.
- A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes.
Other developers report this intermittently as well.
- Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
Requirements
Planned Changes
Litware plans to make the following changes:
- Ensure that agent1 can access all the tools in the environment.
- Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
- Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
- Ensure that Copilot retains details that it has learned and uses that knowledge for future work.
This must be applied to all licensed members of the organization.
Implementation guidelines
The development team at Litware identifies the following implementation guidelines:
- Agent workflows must be able to run in parallel.
- Application error handling must use the repository ErrorHandler class.
- agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
Security requirements
Litware identifies the following security requirements:
- Only the members of SG_Review must be able to approve agent1 plan outputs.
- All API keys must be stored and accessed securely.
- The developers must NOT be able to self-approve.
Agent configuration

You need to provide access to the API key of MCP1. The solution must meet the security requirements.
What should you do?

A. In product-api, add the API key as a GitHub Actions encrypted secret and reference the secret by using ${{ secrets.KEY }} in the workflow YAML of agent1.
B. Store the API key as a secret in the Copilot environment of product-api by using a name prefix of COPILOT_MCP_, and then reference the variable name in the mcp.json configuration.
C. Store the API key as a GitHub Codespaces user secret scoped to product-api.
D. In the product-api repository settings, add the API key directly to the .mcp/server.json file by using a plaintext apiKey field.

正解：B

解説：
Scenario:
Agent environment: A Model Context Protocol (MCP) server named MCP1 is deployed to
https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
MCP1 requires an API key for authentication.
Security requirement: All API keys must be stored and accessed securely.
The correct solution is to store the API key as an Agents secret in the Copilot environment of the repository using the COPILOT_MCP_ name prefix, and then reference it in your MCP configuration.
Strict Prefix Enforcement: GitHub Copilot cloud agent isolates execution for security. It will only expose secrets and variables that explicitly begin with the COPILOT_MCP_ prefix to the MCP server configuration.
Environment Alignment: Storing it as a native Copilot agent secret ensures that when the remote Copilot agent spins up to execute your JSON configuration, it can securely bind and decrypt the secret directly into the server's runtime environment variables.
Config Separation: This practice keeps your sensitive production tokens entirely out of version- controlled mcp.json or .vscode/mcp.json tracking files.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers

質問 # 49
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. copilot-instructions.md
B. agents.md
C. .copilotignore
D. A prompt file (.prompt.md)

正解：D

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 # 50
An agentic coding session in the terminal has grown too long, and you want to discard all prior conversation history and start clean, without summarization. Which slash command should you run?

A. /reset-context
B. /usage
C. /clear
D. /compact

正解：C

解説：
The correct command is /clear. In GitHub Copilot CLI, /clear starts a fresh conversational state by removing the current chat history from the active session. This is the appropriate action when the requirement is to discard prior conversation context completely rather than compress or summarize it.
/compact is specifically designed for context management during long-running sessions. It summarizes older conversation history so that useful information can be retained while freeing context-window capacity. Because the question explicitly says "without summarization," /compact does not satisfy the requirement.
/usage displays session consumption and usage information rather than modifying conversation state. /reset-context is not the standard slash command used for clearing Copilot CLI conversation history in this scenario.
The operational distinction is important in agentic workflows. Compaction preserves semantic continuity, while clearing abandons the prior conversational state and gives the agent a clean starting point. Clearing is therefore appropriate when accumulated context has become irrelevant, misleading, or overly large and you deliberately do not want previous reasoning, instructions, or task history carried forward.
Study Guide Reference Topics: Manage Memory, State, and Execution; conversation-state management; context-window control; session reset versus context compaction.

質問 # 51
......

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