最新Microsoft GH-600考證 - GH-600熱門證照

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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. 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 - 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 - Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Share agent state 3. Prevent conflicting context
Perform evaluation, error analysis, and tuning	15–20%	- Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Refine memory usage 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. Align evaluation criteria with development intent 2. Specify expected outcomes and operational constraints for agent tasks 3. Generate evaluation signals by using automated scanning tools 4. Identify qualitative and quantitative evaluation signals to evaluate agents
Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Block actions that violate defined security, compliance, or Responsible AI policies 2. Preserve execution velocity by minimizing approvals that do not materially reduce risk 3. Identify the subset of actions that require human judgment 4. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 5. Scope permissions and execution contexts to enforce least-privilege access - 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
Prepare agent architecture and SDLC processes	15–20%	- Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Prevent agent action until the agent checks and approves 3. Configure an agent to output a structured plan 4. Configure agent planning to be distinct from agent execution - 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. Configure human intervention for autonomous agents without slowing delivery 3. Plan and implement the degree of agent autonomy, including guardrails
Orchestrate multi-agent coordination	15–20%	- 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. Document key decisions, handoffs, and outcomes across agents 2. Configure multi-agent workflows to produce artifacts suitable for review and audit 3. Perform post-hoc analysis of multi-agent behavior - Operate and manage multi-agent workflows 1. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 2. Apply an orchestration pattern to coordinate multiple agents 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. Add agents to existing multi-agent workflows 3. Retire agents while preserving auditability and workflow continuity
Implement tool use and environment interaction	20–25%	- Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Evaluate the execution context for an agent 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. Configure an agent to be invoked in a CI workflow - Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools - Operate agents with safe execution paths and robust error handling 1. Implement rollbacks 2. Implement escalation paths 3. Implement traceability and accountability for agent actions 4. Implement error handling 5. Implement retries - Configure MCP servers 1. Configure MCP registries 2. Configure a GitHub remote MCP server 3. Configure MCP allow lists 4. Add an MCP server as a tool to an agent

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最新的 GitHub Administrator GH-600 免費考試真題 (Q108-Q113):

問題 #108
Hotspot Question
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests.
You assign an issue to Copilot. Copilot creates a draft pull request. The pull request timeline shows Copilot started work, followed by status updates. The most recent status update is 55 minutes old.
You discover that the agent is no longer making progress.
You need to ensure that the work resumes without redoing the completed steps or changing the previously chosen approach.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Explanation:
Box 1: Select View session from the pull request
To best confirm the agent's progress, troubleshoot the stagnation, and ensure the work resumes seamlessly from where it left off, you should interact directly with the agent's active workspace.
Open the Session View: Navigate to the pull request on GitHub and click the View session button.
This opens the step-by-step agent workflow and reasoning interface.
Inspect the Logs: Review the detailed execution log in the session timeline to identify exactly where the background task (running in GitHub Actions) stalled or encountered an error.
Steer or Prompt to Resume: Instead of unassigning and reassigning the issue (which completely restarts the task from scratch), use the chat input or steering tools directly inside the View session page or Agents panel to nudge Copilot. Providing a clarification prompt allows the agent to continue its current approach without losing completed steps.
Box 2: Post a pull request comment that mentions @copilot
You can resume the work by commenting on the pull request and mentioning @copilot, along with a prompt instructing it to continue.When a GitHub Copilot coding agent stalls or pauses on a draft pull request, posting a comment mentioning @copilot wakes the agent back up. Because the agent evaluates the entire conversation history, code changes, and task checklists within that pull request lifecycle, it will resume from its last saved state. It maintains the previously chosen approach and carries on with the remaining items without repeating already completed steps.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent

問題 #109
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.

答案：

解題說明：

問題 #110
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. From the Copilot CLI, run the /compact command.
B. Start the Copilot CLI and specify the --allow-all parameter.
C. From the Copilot CLI, switch to plan mode.
D. Start the Copilot CLI and specify the --agent=Task parameter.

答案：C

解題說明：
GitHub Copilot CLI plan mode is specifically designed for situations in which an implementation strategy must be developed and reviewed before code modifications begin. In plan mode, Copilot can inspect and analyze the repository, ask clarifying questions, and construct a structured implementation plan while protecting project files from normal editing operations. The resulting plan can then be reviewed and approved before Copilot proceeds with implementation. GitHub explicitly describes plan mode as a mechanism for identifying misunderstandings before code is written and maintaining human control over complex, multi-step work.
Option A is incorrect because --agent=AGENT selects a custom agent; it does not establish a planning-and-approval phase. Option B is the opposite of the requirement: --allow-all grants Copilot broad permission to use tools, paths, and URLs without individual approval. Option D is also incorrect because /compact relates to managing conversation/context size rather than establishing an implementation plan.
Therefore, switching to plan mode is the appropriate first action when the development process requires human review and agreement before changes are applied.
Study Guide Reference Topics: Prepare agent architecture and SDLC processes; human-in-the-loop development; planning before implementation; controlled agent execution.

問題 #111
Hotspot Question
You have a GitHub Enterprise organization that uses GitHub Copilot.
You discover that GitHub Copilot Chat responses in Microsoft Visual Studio Code are influenced by earlier, unrelated troubleshooting prompts from the same conversation.
You need to ensure that the Copilot Chat conversation context is limited to information relevant to the current work item. The solution must minimize effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Explanation:
Box 1: Start a new chat.
Clear the chat session or start a new conversation thread.
Context Reset: Erases the short-term conversation history completely.
Zero Overhead: Requires no configuration changes or administrative interventions.
Fresh State: Forces Copilot to focus only on newly provided code and prompts.
Box 2: Open related files and close unrelated files.
To keep GitHub Copilot Chat focused on the current work item with the minimum amount of effort, you should open related files and close unrelated files.
GitHub Copilot Chat automatically uses the open files and active tabs in your IDE as its immediate context. By closing unrelated files and keeping only relevant code files open, you instantly clean up the context window and force Copilot to focus solely on your current task without needing to modify complex settings.
Reference:
https://docs.github.com/en/copilot/how-tos/troubleshoot-copilot/troubleshoot-common-issues

問題 #112
A team assigns an issue to the GitHub Copilot coding agent by using the following one-line description: Fix the login bug.
Copilot creates a pull request, but the pull request is missing changes and has an incorrect scope.
How should you resolve the issue?

A. Enable Copilot memory.
B. Update the Model Context Protocol (MCP) server rate limits.
C. Add a clear description of the problem to the issue.
D. Allocate more resources to copilot-setup-steps.yml.

答案：C

解題說明：
The primary failure is insufficient task definition. "Fix the login bug" does not identify the observed behavior, expected behavior, reproduction conditions, affected component, or completion criteria. The agent must infer these details, creating a substantial risk of incomplete changes or work outside the intended scope. A clear issue description supplies the information needed to construct and implement an appropriate plan.
For this scenario, the description should identify how the login failure occurs, which authentication path is affected, what successful behavior looks like, and which existing behavior must remain intact. Relevant error messages, reproduction steps, and expected tests make the task independently verifiable. These details turn an ambiguous request into a bounded engineering assignment.
Enabling memory does not supply missing requirements reliably. MCP rate limits govern service interaction rather than task clarity. Additional setup resources address environmental capacity or dependency preparation, not uncertainty about what must change.
The corrective action therefore belongs at task intake, where developers define the agent's inputs and success conditions. The relevant curriculum topics are defining agent inputs, outputs, and success criteria and mitigating poorly scoped task assignments.
Reference:

問題 #113
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