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최신 Claude Certified Developer CCDV-F 무료샘플문제

문제 #1

You are integrating Claude into an application written in Python. The Claude SDK provides a Python client that wraps the underlying REST API.
How would you integrate the SDK?

A. Use a different LLM provider's SDK and translate the responses into Claude's API shape so the application can switch providers in the future.
B. Call the REST API directly with raw HTTP requests so the application avoids the SDK's abstraction between the application code and the API.
C. Use the Claude Python SDK and let it handle authentication, retries, and response parsing through its standard documented patterns for Python integrations.
D. Skip the SDK and embed Claude calls in shell commands invoked from Python, so that the application runs the calls outside the main Python process.

문제 #2

You are building an MCP server that exposes several internal data sources as MCP resources. The server needs to be deployed so multiple Claude applications can integrate with it.
How would you approach the build and deployment?

A. Deploy the MCP server only on individual developer machines, with the Claude applications unable to reach the server outside each developer's machine.
B. Build the MCP server with resource and tool definitions scoped to the first Claude application that needs it, and extend the definitions to additional applications as each integration is requested.
C. Bypass the MCP server and embed each data source directly in every Claude application that needs the data, with each application maintaining its own integration.
D. Author the server with clearly defined resources, tools, and prompts, choose a communication pattern, and deploy to an accessible hosting environment.

문제 #3

Your Claude application uses tool calling to fetch patient data and generate summary reports. The flow occasionally fails because the model returns a tool_use block that references arguments not present in the schema, and your application code does not handle this case gracefully.
How would you address this?

A. Validate the tool_use block's arguments against the tool schema before dispatching the tool and handle invalid arguments as a recognized error path.
B. Log invalid tool_use blocks when they occur and allow the tool dispatch to proceed, relying on the tool's own error handling to surface failures back to the application.
C. Retry the same request repeatedly until the model returns a valid tool_use block that matches the schema as expected.
D. Stop using tool calling entirely and replace tools with prompted text generation that asks the model to describe what it would do.

문제 #4

A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?

A. Tell the teammate that the variation depends on which Claude model serves the request, and recommend pinning the application to a single model version to make the output behavior consistent.
B. Confirm that the variation is a bug in the application's request construction and start investigating which part of the application is producing the inconsistent prompt text.
C. Explain that LLMs generate output token by token, and variation in how prompt content appears in output is a property of generation, not a bug in request construction.
D. Tell the teammate that the variation is caused by the application sending two different prompts on different runs and propose a code change that pins the system prompt to a single version.

문제 #5

You are deciding between Claude models for a task. The team has identified three relevant tradeoff dimensions: quality, latency, and cost.
The right model is the one that...

A. Meets the task's quality requirements at an acceptable latency, with cost reviewed separately once the quality and latency bar has been established.
B. Fits the task's quality, latency, and cost requirements together, recognizing that improving one dimension typically affects the others.
C. Satisfies the task's latency requirement first, then is evaluated against quality and cost thresholds to confirm the selection is acceptable across all three dimensions.
D. Meets the task's cost target within a defined latency budget, with quality validated against a representative sample of inputs after the model is selected.

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