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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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

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 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.
C. A streaming pattern that emits partial decisions as the agent processes each claim, refining the output until a final decision emerges from the stream.
D. A linear chain pattern that processes every claim through fact extraction, then eligibility evaluation, then decision, with no return paths between subtasks.

Question 3

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

Question 4

You are designing a Claude application that maintains user sessions across multi-turn conversations. The product team has asked how the application will handle session lifecycle: when sessions should expire, how state is reset, and how the application avoids carrying stale context into new conversations.
How would you design session lifecycle?

A. Define state reset triggers tied to specific application events but apply them across all sessions globally, with no per-session expiration rules.
B. Define explicit session expiration rules but rely on users to start new conversations when they want fresh context, with no automatic reset triggers in the application.
C. Define explicit session expiration rules, state reset triggers, and rules for starting fresh sessions so stale context does not leak into new conversations.
D. Define a single short session timeout that applies across all conversations and treat the timeout as the application's complete session lifecycle mechanism.

Question 5

Your Claude application's outputs are inconsistent in format. The team's instructions are scattered across the system prompt and user messages, with some instructions stated only once and others repeated.
How would you fix the inconsistency?

A. Consolidate behavioral and format instructions in the system prompt and keep user messages focused on the user's input.
B. Place each instruction wherever the developer who first wrote it found most natural, retaining the original intent.
C. Repeat all instructions in both the system prompt and the user message so the model sees each instruction more than once.
D. Move all instructions into the user message so the application's behavior is controlled at the input point.

Solutions: