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| Section | Weight | Objectives |
|---|---|---|
| Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
| Integrate tools, data, and services | 25% | - Connect data sources and knowledge bases
|
| Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Test, deploy, and monitor agentic AI systems | 20% | - Deploy and monitor agents at scale
|
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NEW QUESTION # 83
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?
Answer: A
Explanation:
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/
NEW QUESTION # 84
You need to configure agent1 to support the planned changes.
What should you do?
Answer: B
Explanation:
The mcp-servers property connects a custom agent to the Model Context Protocol servers required for its planned work. MCP configuration makes defined external tools and services available to the agent in a controlled, declarative manner.
Adding a broad natural-language instruction such as "Use all available tools" does not configure a server connection, authenticate a tool, or define which external capabilities are available. It is also poor least-privilege design because it encourages unrestricted tool use without establishing a technical permission boundary.
Setting tools: [] removes all configured tools and would prevent the agent from using required capabilities. Deleting the tools line could cause the agent to inherit or expose a broader default tool set than intended. Repository-wide instructions can guide behavior, but they do not replace the agent-specific configuration needed to associate MCP servers with that agent.
A well-designed configuration identifies only the MCP servers necessary for the role, supplies secrets through approved environment mechanisms, and limits the resulting tools to the agent's defined task.
Study-guide topics: MCP integration, agent tool configuration, and least-privilege environment access.
NEW QUESTION # 85
Hotspot Question
You have a GitHub repository that uses the following GrtHub Copilot CLI command in a Bash script.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Yes
Setting --max-autopilot-continues 10 acts as a hard ceiling, preventing infinite loops by cutting off the execution the moment it reaches the 10-step limit.
Box 2: Yes
This command will allow the agent to use all local tools without prompting you for permission.
The --yolo flag is a built-in alias in the official GitHub Copilot CLI. It bypasses safety confirmation prompts by combining three specific permission-granting arguments: --allow-all-tools, --allow-all- paths, and --allow-all-urls.
Box 3: No
This specific command will not allow targeted human intervention at key decision points because it explicitly strips away all prompt checkpoints The options configuration used in your script forces the GitHub Copilot CLI to bypass user confirmation entirely and execute the objective fully autonomously Reference:
https://pub.towardsai.net/i-stopped-prompting-github-copilot-and-started-delegating-to-it-fe2f12a21709?gi=fbf268b2a564
https://docs.github.com/en/copilot/concepts/agents/copilot-cli/autopilot
NEW QUESTION # 86
In Microsoft Visual Studio Code, you use GitHub Copilot agent mode for a repository. The GitHub Model Context Protocol (MCP) server is enabled.
Your team maintains an internal orchestrator script that creates independent Copilot SDK sessions to process operational events from webhooks.
You discover that the agent sometimes mixes context between unrelated webhook deliveries. Additionally, in some cases, the agent starts running tools before a human reviews the proposed steps.
You need to ensure that the script meets the following business requirements:
Planning must be kept separate from executing.
Each plan must be captured without calling any tools.
Each webhook event must create a new independent session.
Once the plan is complete, tool calls must be enabled within the same event-specific session for follow-up.
Answer:
Explanation:
NEW QUESTION # 87
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?
Answer: B
Explanation:
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.
NEW QUESTION # 88
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