Anthropic CCDV-F 시험 개요:

Anthropic CCDV-F 시험 요강 주제:

최신 Claude Certified Developer CCDV-F 무료샘플문제

문제 #1
The Anthropic API deprecated a request parameter that your Claude application uses in approximately 40 places across the codebase. The deprecation notice gives a six-month window before the parameter is removed and recommends a replacement parameter with slightly different semantics.
You would respond to the deprecation by...

A. Adding regression tests to cover the parameter's behavior, then migrating call sites in batches that you validate against regression tests.
B. Keeping the deprecated parameter in place while writing a wrapper function around it to insulate the rest of the codebase from the eventual change.
C. Migrating all 40 call sites in a single change near the removal date to ensure the deprecated parameter continues to work as long as possible.
D. Swapping all 40 call sites in a single change right away to prevent drawn-out migration work that will delay ongoing functioning.

문제 #2
A teammate has asked you to explain the difference between context engineering and prompt engineering.
They have heard the terms used interchangeably and are unsure how each applies to a Claude application that processes long-running multi-step tasks.
How would you describe the distinction?

A. Prompt engineering and context engineering each address content the team gives Claude, but the team can group them under a single workflow because the practices use overlapping techniques.
B. Prompt engineering focuses on the model's response, while context engineering focuses on the user's input across many sessions in a long-running multi-step Claude application.
C. Prompt engineering is the older term for prompt design, while context engineering is the newer term that has replaced it in modern Claude applications across the industry.
D. Prompt engineering shapes individual prompts for specific outputs, while context engineering manages how content flows across turns and steps and takes steps to keep relevant state visible.

문제 #3
Your agent makes 10 to 15 tool calls per task, and you have noticed it sometimes loses track of earlier results by the time it reaches later steps. The agent's context window is large enough to hold all the messages, but the relevant information appears to get buried as the conversation grows.
How would you address this?

A. Increase the context window further so all tool outputs from every prior step remain in full detail throughout the task.
B. Reduce the number of tool calls per task by combining several existing tools into larger, multi-purpose tools.
C. Switch to a different agentic framework that advertises automatic context-window management as a built-in feature.
D. Apply a context-management pattern that summarizes or prunes older tool outputs while preserving the active task state.

문제 #4
Your team is integrating Claude into an existing REST API service. The service handles concurrent requests, and you are deciding how to structure the Claude API calls within the existing async codebase.
How would you structure the Claude calls?

A. Replace the existing async REST service with a synchronous service to match the call style of synchronous Claude SDK clients.
B. Run Claude API calls in a separate thread pool isolated from the rest of the service so the main event loop is not affected.
C. Make synchronous Claude API calls and block the async event loop until each call completes before processing the next request.
D. Use the Claude SDK's async client and structure Claude calls as awaitable operations alongside the rest of the service's async code.

문제 #5
You are starting a new Claude application and have a small set of well-labeled examples that demonstrate the desired output format. You want to use these examples to guide Claude's behavior.
How would you guide the application's behavior?

A. Use zero-shot prompting and rely on the model's general capability to produce the desired output format consistently across all incoming requests.
B. Train a custom model on the labeled examples before deployment so that the application does not need to include the examples in any prompt during operation.
C. Embed the examples in a database and retrieve them at runtime as reference material for the team.
D. Use multi-shot prompting by including the labeled examples in the prompt so Claude can match the desired output format on each request.

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