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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude application's prompt was written months ago and has not been updated. The team has discovered through evals that the prompt produces good results on common cases but underperforms on a specific category of inputs that has grown in volume.
How would you respond?

A. Iterate on the prompt to address the underperforming category, validate the change with evals, and continue refining as needed.
B. Tell users to avoid the underperforming category by adding warnings in the application's user interface about handled inputs.
C. Add the underperforming category to a separate Claude application with its own prompt so the original prompt does not change.
D. Replace the prompt with a new one aligned to the underperforming category, treating any common-case performance change as a known tradeoff.

Question 2

A teammate has asked you to explain why the team's Claude application is billed for output tokens at a different rate than input tokens. They had assumed the rate was the same for both.
How would you explain the difference?

A. Output tokens are typically billed at the same rate as input tokens, and the apparent rate difference is a billing error to report to Anthropic.
B. Output tokens are not billed at all, because cost is determined entirely by the input tokens sent to the model on each request.
C. Output tokens are typically billed at a lower rate than input tokens, because output tokens are cheaper to produce than input tokens are to process.
D. Output tokens are typically billed at a higher rate than input tokens, and cost models for the application should reflect both rates separately.

Question 3

Your Claude application is deployed to development, staging, and production environments. Each environment uses a different model version, different prompt versions, and different plugin dependencies, but the configuration is currently scattered across environment variables, hardcoded values, and undocumented setup scripts.
How would you manage the configuration?

A. Standardize all environments to use the same configuration values to eliminate the differences between development, staging, and production.
B. Use the latest available model version everywhere and stop pinning model versions, on the grounds that pinning adds maintenance overhead the team should aim to reduce.
C. Move all configuration into hardcoded application code to reduce reliance on external configuration sources that are difficult to track over time.
D. Consolidate the configuration into a version-controlled system documenting model version pinning, prompt versioning, and plugin dependencies for each environment.

Question 4

Your Claude application is producing inconsistent outputs for similar inputs, even when using the same model and prompt. You want to debug the issue systematically.
Your debugging approach would...

A. Capture full traces of input, system prompt, user messages, model output, and parameters, then analyze the differences between consistent and inconsistent runs.
B. Assume inconsistent outputs reflect normal LLM non-determinism and document the variability as an accepted characteristic of the application's behavior.
C. Lower the model's temperature and re-run the inconsistent inputs to determine whether the parameter change reduces output variability across runs.
D. Retry every request that produces an unexpected output and log the results to identify whether repeated calls converge on a consistent response pattern.

Question 5

Your Claude application returns confident-sounding answers, but occasionally those answers contain factual errors that downstream systems treat as ground truth. The team is concerned about the application's confidence-versus-accuracy gap.
How would you address the gap?

A. Apply skepticism toward confident output by adding validation steps, sourcing requirements, or confidence calibration before treating outputs as ground truth.
B. Reject every response the application produces until a manual accuracy review is conducted on each response by a human reviewer before any downstream system uses it.
C. Add a disclaimer to every output telling users to verify the accuracy of the output and treat the disclaimer as the primary mechanism for managing the confidence-versus-accuracy gap.
D. Lower the model's temperature so the model's responses sound less confident and downstream systems are less likely to treat the responses as ground truth in normal operation.

Solutions: