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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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

Question 2

Your Claude application makes high-volume API calls during business hours and very few calls overnight.
The team is concerned about staying within rate limits during peak hours and wants to understand how the Claude API enforces those limits.
How would you proceed?

A. Identify the rate limits, design the application to stay within them during peak hours, and use exponential backoff when limits are reached.
B. Review the API documentation for streaming endpoints and evaluate whether migrating peak-hour calls to streaming reduces exposure to rate limit enforcement.
C. Examine the peak-hour request patterns in your application logs and smooth traffic by distributing requests more evenly across the business-hours window.
D. Assess the average payload size of current API calls and consolidate requests where possible to reduce the total number of calls made during peak hours.

Question 3

Your Claude application requests structured JSON output from the model. Most of the time the JSON is well- formed, but occasionally Claude returns malformed JSON that breaks downstream processing.
How would you handle the malformed output?

A. Manually inspect every response before downstream processing so a human reviewer catches any malformed JSON before the application passes the response to downstream systems.
B. Retry the same request repeatedly until valid JSON appears in the model's response, with the retry loop adding delay to the application's response time on affected requests.
C. Add output validation that parses Claude's response against the expected schema and treats malformed output as a recognized error path with retry or fallback handling.
D. Switch to free-form text output so the application no longer depends on JSON parsing for any of the responses it sends to downstream systems during normal operation.

Question 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. Make synchronous Claude API calls and block the async event loop until each call completes before processing the next request.
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. Replace the existing async REST service with a synchronous service to match the call style of synchronous Claude SDK clients.
D. Use the Claude SDK's async client and structure Claude calls as awaitable operations alongside the rest of the service's async code.

Question 5

You are building a Claude application that needs to deliver model output to end users as it is generated, instead of waiting for the full response to complete.
The Claude API mechanism you would use is...

A. Prompt caching, which speeds up the cost profile of future requests and does not affect the delivery timing of the first response.
B. The Batch API, which delivers full responses after a delay suitable for non-interactive workloads.
C. Structured JSON output, which delivers responses only after the model has finalized the JSON shape across the entire response.
D. Streaming responses, which deliver tokens incrementally as the model generates them so users see output progressively.

Solutions: