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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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

Question 2

You are establishing the guardrail strategy for a Claude application. The team wants to ensure guardrail failure does not expose the application to unsafe behavior.
The guardrail strategy would...

A. Layer multiple guardrails so a single guardrail failure does not expose the application to unsafe behavior.
B. Apply guardrails at the application output level only and route flagged responses to a human reviewer before they are delivered to the user.
C. Implement a single comprehensive system prompt guardrail and validate its coverage against the application's full range of expected inputs.
D. Apply guardrails at the model level only and rely on the model's built-in safety behaviors to handle any cases the guardrail does not explicitly cover.

Question 3

You are designing an agent that handles a multi-step research task. You want the agent to break the task into smaller pieces, hand each piece to a focused subagent, and consolidate the results.
The agent pattern you would apply is...

A. A memory pattern that stores the entire research history in advance, before any subtask begins execution.
B. A single tool-use loop that includes every tool the agent might need across all subtasks.
C. A context-window pruning pattern that drops each subtask's content after the agent moves on.
D. An orchestrator and subagent pattern with specialized subagents assigned to each subtask.

Question 4

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

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. 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.
B. 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.
C. 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.
D. Apply skepticism toward confident output by adding validation steps, sourcing requirements, or confidence calibration before treating outputs as ground truth.

Solutions: