Microsoft GH-600オンライン試験 & GH-600模擬トレーリング

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Perform evaluation, error analysis, and tuning	15-20%	- Tune agent behavior based on evaluation results 1. Refine memory usage 2. Refine tool usage and tool access 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 - Define success criteria and evaluation signals for agent tasks 1. Specify expected outcomes and operational constraints for agent tasks 2. Generate evaluation signals by using automated scanning tools 3. Align evaluation criteria with development intent 4. Identify qualitative and quantitative evaluation signals to evaluate agents
Implement tool use and environment interaction	20-25%	- Operate agents with safe execution paths and robust error handling 1. Implement escalation paths 2. Implement traceability and accountability for agent actions 3. Implement error handling 4. Implement rollbacks 5. Implement retries - Configure MCP servers 1. Configure MCP allow lists 2. Configure a GitHub remote MCP server 3. Configure the MCP registries 4. Add an MCP server as a tool to an agent - Select and configure agent tools 1. Configure agent tools 2. Identify required 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 handle environment-specific constraints 3. Configure an agent to use branch-based scope 4. Evaluate the execution context for an agent 5. Configure an agent to be invoked in a CI workflow 6. Enable an agent to perform autonomous actions, including creating branches and pull requests
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. Identify the subset of actions that require human judgment 2. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 3. Preserve execution velocity by minimizing approvals that do not materially reduce risk 4. Block actions that violate defined security, compliance, or Responsible AI policies 5. Scope permissions and execution contexts to enforce least-privilege access
Orchestrate multi-agent coordination	15-20%	- 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 - 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. Configure agent isolation for parallel execution 3. Apply an orchestration pattern to coordinate multiple agents - Manage the lifecycle of agents within multi-agent workflows 1. Add agents to existing multi-agent workflows 2. Update, reconfigure, or replace agents without disrupting active workflows 3. Retire agents while preserving auditability and workflow continuity
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 - Implement agent memory strategies 1. Define memory expiration, pruning, and reset rules 2. Choose between short-term, long-term, and external memory 3. Scope agent memory to task-relevant information - 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
Prepare agent architecture and SDLC processes	15-20%	- Define boundaries between planning, reasoning, and action 1. Prevent agent action until the agent checked and approved 2. Validate agent plans 3. Configure agent planning to be distinct from agent execution 4. Configure an agent to output a structured plan - Integrate agents into the software development lifecycle (SDLC) 1. Identify steps for agents to perform 2. Identify and mitigate common anti-patterns in agents 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

>> Microsoft GH-600オンライン試験 <<

Microsoft GH-600模擬トレーリング、GH-600日本語対策問題集

クライアントがGH-600テストに合格すると、多くのメリットがあります。 GH-600試験の練習教材が提供する知識は、クライアントの実際の作業能力と知識の蓄積を高めるのに役立つため、クライアントは賃金を上げて上司に昇進させることが容易になります。また、彼らは同僚、友人、家族から尊敬され、業界のエリートとして認められます。彼らはさらなる研究のために海外で働くためのより多くのアクセスを獲得します。そのため、クライアントは、テストに合格した後、GH-600調査の質問に感謝しなければなりません。

Microsoft Developing in Agentic AI Systems 認定 GH-600 試験問題 (Q100-Q105):

質問 # 100
You have a GitHub repository that uses GitHub Actions to run an autonomous coding-agent workflow.
Your team requires that session logs be captured as workflow artifacts so that agent runs can be audited and traced back to a specific execution and code state.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

正解：

解説：

質問 # 101
You have a GitHub Enterprise Cloud organization that has Copilot agents enabled.
You discover activity in a repository.
You need to identify whether the activity was initiated by an agent.
Which two audit log fields can you use? Each correct answer presents a complete solution.

A. agent_session_id
B. user
C. action
D. actor_is_agent

正解：A、D

質問 # 102
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. opens a pull request that has the plan as a comment
C. creates a branch named /copilot/plan in the repository
D. begins the implementation

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 # 103
You are about to start a complex refactoring task in the GitHub Copilot CLI.
Before Copilot makes any changes, you need to review and agree on the approach.
What should you do first?

A. Start the Copilot CLI and specify the --allow-all parameter.
B. Start the Copilot CLI and specify the --agent=Task parameter.
C. From the Copilot CLI, run the /compact command.
D. From the Copilot CLI, switch to plan mode.

正解：D

解説：
The first action you should take is to switch to Agent mode or use the Plan agent to review, edit, and approve a detailed step-by-step Markdown plan before any code is modified.
Use the Plan Agent: If available in your environment, trigger the plan phase so Copilot scans your codebase in a read-only state to outline its entire approach first.
Review the Proposed Plan: Carefully inspect the resulting Markdown structure to catch any architectural issues or wrong directions before the "doing" phase begins.
Utilize edit Mode Intentionally: Alternatively, if you prefer granular control over file adjustments rather than a fully autonomous agent workflow, opt for edit mode. This allows you to specifically select the target files and describe the natural language updates manually.
Reference:
https://aidevme.com/think-before-you-build-github-copilots-plan-agent-in-visual-studio-structured-ai-assisted-development/

質問 # 104
Hotspot Question
You have a GitHub repository that contains the following custom agent files:
- A file named planner.agent.md that includes YAML frontmatter with a
handoffs entry that has label: Start Implementation, agent:
implementer, and prompt: Now implement the plan outlined above
- A file named implementer.agent.md that is in the same directory as
planner.agent.md and includes YAML frontmatter that has name:
IMPLEMENTER
You add a third agent file named review.agent.md.review.agent.md includes YAML frontmatter that has name: code-review.
You make the following changes to planner.agent.md:
- Update the existing handoff to include send: true and model: GPT-5.2
(copilot).
- Add a second handoff that has label: Run Review, agent: code-review,
and prompt: Review the code changes made in the previous step.
No other agent files are modified.
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
GitHub Copilot Extensions and agent frameworks route handoffs by matching the agent identifier in the YAML frontmatter to the filename or the name property defined within that repository.
Box 2: Yes
The failure happens because the agent target pointer in the handoff configuration does not match the actual target agent's declaration or file name due to a mismatch introduced by your changes.
Box 3: Yes
Yes, selecting "Start Implementation" will switch the chat to the implementer agent and automatically submit the prompt using the specified model.
The updates made to your GitHub agent repository configure a seamless workflow transition.
Here is how your new settings process that action:
Trigger action: Clicking Start Implementation activates the specific handoff block you modified.
Agent switch: The agent: implementer property targets the file with name: IMPLEMENTER, routing the conversation there.Automated submission: Setting send: true forces the interface to submit the text immediately instead of leaving it in the text box.
Model override: The model: GPT-5.2 (copilot) line forces the system to process that automated submission using that specific model version.
Reference:
https://arinco.com.au/blog/github-copilot-multi-repo-instructions/
https://aminrj.com/posts/building-a-multi-agents-coding-workflow/

質問 # 105
......

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GH-600模擬トレーリング: https://www.jpntest.com/shiken/GH-600-mondaishu