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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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

Question 2

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. Run two copies of the agent in parallel for every task and use whichever one finishes first as the source of truth.
C. Restart the task from the beginning whenever a process is interrupted.
D. Increase the agent's timeout to several hours so that interruptions become rare enough to ignore in practice.

Question 3

You are setting up Claude Code for a new project repository. Your team has shared coding standards, preferred libraries, and project-specific context that every developer working on the repository should have available when they use Claude Code.
How would you set this up?

A. Configure Claude Code through environment variables that each developer sets on their own machine when they begin working in the repository.
B. Initialize Claude Code in the repository and document the standards and project-specific context in a CLAUDE.md file at the repository root.
C. Document the standards in a separate wiki page maintained outside the repository so the documentation stays decoupled from the source code.
D. Add the coding standards and project context to the repository's existing README file and direct developers to reference it when starting Claude Code sessions.

Question 4

You are setting up a Claude application that will run a mix of multi-turn conversations and one-off requests.
You want to use caching techniques to reduce token costs where they apply. A teammate suggests caching the model's output as well, so the application does not have to make duplicate Claude calls when similar queries arrive.
You would apply prompt caching to...

A. The static portions of prompts that are repeated across requests, such as system prompts, instructions, or shared context.
B. The model's output, treating the response from each request as cacheable content the application can return on similar future queries.
C. Only the user's input portion of each request because user input is the part of the prompt that varies the most across the application's normal operation.
D. Nothing, because prompt caching does not affect cost in any application that mixes multi-turn conversations and one-off requests in a single deployment.

Question 5

You are implementing a custom tool for your Claude agent. The tool needs to interact with an external pricing service that returns product data.
Which of the following best practices would you apply as you develop this tool?

A. Omit the tool description and let the model infer when to use the tool based on the tool's name and the rest of the prompt context.
B. Implement the tool with no error handling and let the agent loop catch failures whenever the pricing service returns an error during operation.
C. Define the tool with a clear schema, write a precise description for when to call it, and handle pricing service errors explicitly.
D. Define the tool with a loose schema and let the model interpret the inputs flexibly on each call the agent makes.

Solutions: