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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your team uses several plugins across multiple Claude applications, and a recent plugin update introduced a regression. The team had not been tracking plugin versions, so the team cannot easily identify which version was previously working. How would you address this?

A. Upgrade every plugin to the latest version on a regular cadence to keep version drift small, on the grounds that drift contributes to regression risk.
B. Stop using all plugins until the team can rebuild equivalent functionality directly into the application code, treating plugin avoidance as a way to remove version-related risk.
C. Add explicit plugin version tracking to the project's configuration so the team can identify, pin, and upgrade plugin versions deliberately.
D. Treat plugins as untrackable third-party code and rely on plugin authors to communicate breaking changes when they happen, with no internal version tracking.

Question 2

You are designing an agent that handles a complex claim-processing workflow. Each claim moves through fact extraction, eligibility evaluation, and a decision step. The three subtasks have distinct success criteria, and some claims require iteration between fact extraction and eligibility evaluation before a decision can be reached.
Which agent pattern would you apply?

A. A single tool-use loop pattern that gives one agent access to all the tools needed for fact extraction, eligibility evaluation, and decision-making.
B. A linear chain pattern that processes every claim through fact extraction, then eligibility evaluation, then decision, with no return paths between subtasks.
C. A graph-based pattern that lets the agent move between subtasks based on the state of each claim, with each subtask evaluated against its own criteria.
D. A streaming pattern that emits partial decisions as the agent processes each claim, refining the output until a final decision emerges from the stream.

Question 3

Your team is integrating Claude into an existing REST API service. The service handles concurrent requests, and you are deciding how to structure the Claude API calls within the existing async codebase.
How would you structure the Claude calls?

A. Make synchronous Claude API calls and block the async event loop until each call completes before processing the next request.
B. Run Claude API calls in a separate thread pool isolated from the rest of the service so the main event loop is not affected.
C. Use the Claude SDK's async client and structure Claude calls as awaitable operations alongside the rest of the service's async code.
D. Replace the existing async REST service with a synchronous service to match the call style of synchronous Claude SDK clients.

Question 4

Your Claude application runs long agentic workflows where the agent makes many tool calls, and the conversation history grows quickly. After about 20 tool calls, you notice the agent's responses become less focused and sometimes ignore earlier task constraints.
How would you address this?

A. Apply context engineering techniques such as tool output pruning or compaction to keep the active task state visible while reducing the volume of older content.
B. Restart the agent every five tool calls to prevent any drift, with the agent losing all task state at each restart point during the workflow.
C. Remove tool calling from the workflow entirely so the agent operates as a single text-generation step with no tool outputs accumulating in the context window.
D. Increase the model's context window so the agent can hold every tool output at full detail across the entire workflow no matter how many tool calls it accumulates.

Question 5

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 the same rate as input tokens, and the apparent rate difference is a billing error to report to Anthropic.
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 a higher rate than input tokens, and cost models for the application should reflect both rates separately.

Solutions: