最新のCCDV-F問題集で試験を準備する

You are setting up a Claude application that will run a mix of multi-turn conversations and one-off requests.
You want to use caching techniques to reduce token costs where they apply. A teammate suggests caching the model's output as well, so the application does not have to make duplicate Claude calls when similar queries arrive.
You would apply prompt caching to...

• A. The model's output, treating the response from each request as cacheable content the application can return on similar future queries.
• B. Nothing, because prompt caching does not affect cost in any application that mixes multi-turn conversations and one-off requests in a single deployment.
• C. The static portions of prompts that are repeated across requests, such as system prompts, instructions, or shared context.
• D. Only the user's input portion of each request because user input is the part of the prompt that varies the most across the application's normal operation.

You are implementing a custom tool for your Claude agent. The tool needs to interact with an external pricing service that returns product data.
Which of the following best practices would you apply as you develop this tool?

• A. Define the tool with a loose schema and let the model interpret the inputs flexibly on each call the agent makes.
• B. Omit the tool description and let the model infer when to use the tool based on the tool's name and the rest of the prompt context.
• C. Implement the tool with no error handling and let the agent loop catch failures whenever the pricing service returns an error during operation.
• D. Define the tool with a clear schema, write a precise description for when to call it, and handle pricing service errors explicitly.

A teammate has asked how to extend Claude Code with a custom Skill that the team can invoke during sessions. The Skill consists of a set of instructions and a few support scripts the team wants Claude to be able to call when the Skill is loaded.
Where is the right place to define the Skill?

• A. Define the Skill as a long inline instruction at the top of every CLAUDE.md file in the team's repositories so Claude has access to it on every session.
• B. Define the Skill in a personal scratch directory on each developer's machine and load it manually before each Claude Code session that needs it.
• C. Define the Skill inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.
• D. Define the Skill in a Skills directory recognized by Claude Code, where Claude can discover and load it during sessions for the team's repositories.

Your Claude application produces good responses for typical inputs but struggles with edge cases. You have several labeled examples of edge-case inputs and the desired response for each. You want to use these examples to improve the model's handling of edge cases.
What is the best way to use these examples?

• A. Tell users to avoid submitting the edge-case inputs to the application by adding warnings in the application's user interface.
• B. Add the labeled edge-case examples to the prompt as few-shot examples so the model can learn the pattern.
• C. Embed the examples in a database for the model to find during inference.
• D. Train a custom model on the edge-case examples and deploy that custom model in place of the team's current Claude integration.

Your Claude application has multi-step workflows where each step's output is needed only briefly before the agent moves on. The cumulative tool output is filling the context window with content that is no longer relevant.
How would you handle the accumulating tool output?

• A. Switch to a smaller Claude model that processes context more efficiently and treat any quality loss as a tradeoff for the cost reduction.
• B. Keep every tool output in the context indefinitely so the agent has the full record of every step it has executed during the workflow.
• C. Apply tool output pruning to remove tool outputs that are no longer needed by later steps in the workflow.
• D. Apply prompt caching to the accumulated tool outputs so the application does not re-pay for the older content on each subsequent step.