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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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. 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.
D. Implement a single comprehensive system prompt guardrail and validate its coverage against the application's full range of expected inputs.

Question 2

You are building a Claude application that processes 10,000 customer emails overnight to extract structured data. The work is non-interactive, runs once daily, and has a flexible completion window of several hours.
Which Claude API would you use?

A. The real-time Messages API with concurrent requests to process the emails as fast as possible during the overnight window.
B. The Batch API, which is designed for non-interactive workloads with flexible completion windows.
C. The real-time Messages API, processing the emails one at a time sequentially to ensure consistent ordering of results.
D. The streaming responses API to process each email and return partial results to a database as the model generates them.

Question 3

A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?

A. Increase the size of the agent's context window so it can hold the full ticket history at once.
B. Add detailed prompting that instructs the agent to track which sub-issues have been resolved and which remain.
C. Introduce an orchestrator agent that delegates billing, shipping, and product sub-issues to dedicated subagents.
D. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.

Question 4

You are designing a Claude application that will process customer support tickets in two stages: a triage stage that classifies tickets and a response stage that drafts replies. The team is debating whether to use a single Claude call that handles both stages or separate Claude calls for each stage.
How would you structure the application?

A. Use a single Claude call for both stages, on the grounds that a single call is cheaper than multiple calls in any production Claude application setup.
B. Use separate Claude calls for triage and response, because each stage has distinct inputs, outputs, and success criteria that benefit from focused prompts.
C. Use multiple Claude calls in parallel that each draft a complete ticket reply, then have a fourth Claude call select the best one to send to the customer.
D. Use a single Claude call for triage and then use a non-Claude rule-based system for response generation, on the grounds that rule-based systems are more reliable for drafting replies.

Question 5

Your team is preparing a new Claude application for production, and the product team has asked for a cost projection. The team needs to estimate the cost based on expected request volume, average input length, and average output length. How would you build the projection?

A. Build a cost model that combines expected request volume and average input tokens, treating output tokens as a small enough share of cost to leave out of the projection.
B. Build a cost model that uses the average per-request cost from a similar Claude application the team built last year, scaling that figure by expected request volume.
C. Build a cost model based on expected request volume and the chosen model's pricing, treating average input and output token counts as variables to be estimated post-launch.
D. Build a cost model that combines expected request volume, average input tokens, average output tokens, the chosen model's pricing, and any caching benefits.

Solutions: