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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. Embed the examples in a database for the model to find during inference.
B. Train a custom model on the edge-case examples and deploy that custom model in place of the team's current Claude integration.
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 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. 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.
B. 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.
C. Apply skepticism toward confident output by adding validation steps, sourcing requirements, or confidence calibration before treating outputs as ground truth.
D. 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.

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

The product team has asked you to choose a Claude model for a new feature. The team has provided functional requirements but has not specified performance, cost, or quality targets. The team's product manager says, "Use whatever model gives us the best results." How would you respond?

A. Ask the product team to specify quality, latency, and cost targets, then select the model whose tradeoffs best fit those targets.
B. Choose the largest, highest-capability Claude model, on the grounds that "best results" is most likely to mean highest quality.
C. Choose a mid-tier model and ship the feature, because mid-tier models work for most use cases without specified targets.
D. Run every Claude model on a representative sample and pick whichever scores best on a generic benchmark.

Question 5

Your agent is processing tasks that take 30 to 60 minutes to complete. Each task has well-defined intermediate checkpoints, and the team wants the agent to be able to resume from the most recent checkpoint if a process is interrupted.
How would you implement this resumability?

A. Apply a checkpointing pattern that persists the agent's intermediate state and reloads that state when resuming an interrupted task.
B. Restart the task from the beginning whenever a process is interrupted.
C. Run two copies of the agent in parallel for every task and use whichever one finishes first as the source of truth.
D. Increase the agent's timeout to several hours so that interruptions become rare enough to ignore in practice.

Solutions: