GH-600試験概要 & GH-600受験対策解説集

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Manage memory, state, and execution	10–15%	- 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 - Persist agent state and manage context drift 1. Resume agent work without repeating steps or diverging from prior decisions 2. Capture task progress and decisions as durable artifacts 3. Detect and correct drift during extended agent execution - Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Share agent state 3. Prevent conflicting context
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. Plan and implement the degree of agent autonomy, including guardrails 3. Configure human intervention for autonomous agents without slowing delivery - 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 - 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
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. 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 - Tune agent behavior based on evaluation results 1. Refine memory usage 2. Revise instructions, workflows, or constraints 3. Refine tool usage and tool access - 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
Implement tool use and environment interaction	20–25%	- Select and configure agent tools 1. Configure agent tools 2. Identify required tools 3. Configure agent tool permissions - Configure MCP servers 1. Configure a GitHub remote MCP server 2. Configure MCP allow lists 3. Configure MCP registries 4. Add an MCP server as a tool to an agent - Operate agents with safe execution paths and robust error handling 1. Implement escalation paths 2. Implement retries 3. Implement traceability and accountability for agent actions 4. Implement error handling 5. Implement rollbacks - Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Enable an agent to perform autonomous actions, including creating branches and pull requests 3. Configure an agent to handle environment-specific constraints 4. Configure an agent to be invoked in a CI workflow 5. Configure an agent to use branch-based scope 6. Evaluate the execution context for an agent
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. Identify the subset of actions that require human judgment 2. Preserve execution velocity by minimizing approvals that do not materially reduce risk 3. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 4. Scope permissions and execution contexts to enforce least-privilege access 5. Block actions that violate defined security, compliance, or Responsible AI policies
Orchestrate multi-agent coordination	15–20%	- 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 - 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. 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 - 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

>> GH-600試験概要 <<

GH-600受験対策解説集 & GH-600最新な問題集

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Microsoft Developing in Agentic AI Systems 認定 GH-600 試験問題 (Q105-Q110):

質問 # 105
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. /yolo
C. /context
D. /clear

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 # 106
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests. The repository uses GitHub Actions for CI, and reviewers rely on pull request timelines and workflow artifacts to understand what the agent did.
During long-running agent tasks, the reviewers lose track of decisions and validation steps, which causes repeated questions and reworks when context drifts between iterations.
You need to persist task progress and decisions as durable artifacts and ensure that the reviewers can verify what the agent did during and after execution by using GitHub as the system of record.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action 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：

質問 # 107
You have a GitHub Enterprise repository.
An agent opens pull requests to the main branch.
You need to ensure that changes to .github/workflows/* and /infra/* require approval from designated reviewers before merge.
What should you configure?

A. a ruleset and a .copilotignore file
B. a ruleset and an agents.md file
C. a branch protection rule and a CODEOWNERS file
D. a branch protection rule and copilot-instructions.md

正解：C

解説：
To secure these specific directories in your GitHub Enterprise repository, you need to configure CODEOWNERS combined with a Repository Branch Protection Rule.
Reference:
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/approving-a-pull-request-with-required-reviews

質問 # 108
You use a GitHub Actions workflow to orchestrate a multi-agent draft-review process for pull requests. The workflow uploads a single combined review-package artifact at the end of each run.
You discover that the review-package artifact is occasionally missing from the workflow run history, and you suspect that a user deleted it.
You need to identify which user manually deleted a workflow run artifact and when the deletion occurred.
What should you use?

A. the artifact.destroy audit log event
B. the checks.delete_logs audit log event
C. the merge commit history
D. the pull request description and comment history

正解：A

解説：
You should check the artifact.destroy audit log event.
This specific event tracks whenever a user or a token manually deletes a workflow run artifact. It provides exactly what you need to troubleshoot why your multi-agent review packages are disappearing.
Reference:
https://docs.github.com/en/organizations/managing-organization-settings/configuring-the-retention-period-for-github-actions-artifacts-and-logs-in-your-organization

質問 # 109
You are running a long GitHub Copilot CLI session. The session has been running for several hours, and you have made significant progress on a multi-file implementation.
Suddenly, you see the Compaction completed message appear without running a command.
What caused the compaction to occur?

A. The agent automatically switched to a model that has a larger context window.
B. The session exceeded the maximum allowed duration.
C. The session state file reached the maximum file size.
D. The conversation approached 95 percent of the context-window capacity.

正解：D

解説：
Automatic compaction occurs when the conversation approaches the context-window threshold. The system summarizes or compresses earlier context so that the session can continue without exceeding the model's available context capacity.
Compaction is driven by conversational context volume, not by the size of a local state file or elapsed session duration. It also does not mean that the agent switched models. The purpose is to retain the most relevant working information while reducing the amount of prior detail that must remain in the active context.
For long implementation sessions, developers should preserve important decisions in durable artifacts such as repository files, task lists, plans, issues, and commits. This reduces reliance on the conversation retaining every implementation detail verbatim. After compaction, the developer should verify the current plan, changed files, and pending tasks before authorizing consequential operations.
Study-guide topics: context-window management, compaction, durable state, and long-running agent sessions.

質問 # 110
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