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Anthropic CCDV-F Exam Overview:

Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?

A. Adjust the temperature parameter together with the max_tokens parameter, treating the combined adjustment as the team's mechanism for managing context window pressure during long-transcript processing.
B. Remove the system prompt entirely to make room for longer transcripts in each request, freeing up context window space the system prompt would otherwise consume.
C. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
D. Increase the temperature parameter as the junior developer suggested and observe whether the context window issue resolves over the next several runs of the application in production.

Question 2

Your Claude agent performs database operations. A recent incident occurred where the agent ran a destructive query that affected production data. The team wants to add deterministic controls to prevent similar incidents.
How would you prevent similar incidents?

A. Switch to a higher-capability Claude model on the grounds that a more capable model is less likely to run destructive queries during normal operation across all requests.
B. Run the agent only during business hours when humans are available to monitor its activity, treating the schedule as the primary control mechanism for destructive operations.
C. Add Claude hooks that intercept database operations and apply deterministic checks, such as blocking destructive queries or requiring approval, before the queries execute.
D. Add a system prompt instruction telling the agent to be careful with database operations on every request the application handles during normal operation across all incoming traffic.

Question 3

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

Question 4

Your enterprise has a contract with AWS that requires Claude API calls to flow through Amazon Bedrock rather than the direct Anthropic API. Your team is building a new Claude application and is unfamiliar with this constraint.
How would you build the application?

A. Build the application against the direct Anthropic API now and migrate to Bedrock in a follow-up release once the team has more experience with the Bedrock API.
B. Configure the application to invoke Claude through the Bedrock-compatible API path while keeping the application's logic provider-agnostic.
C. Build two parallel implementations of every call, one for the direct Anthropic API and one for Bedrock, and pick the faster one at runtime.
D. Build the application against the direct Anthropic API and ignore the contractual requirement to route Claude calls through Amazon Bedrock.

Question 5

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

Solutions: