Anthropic Claude Certified Developer-Foundations : CCDV-F

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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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 and ignore the contractual requirement to route Claude calls through Amazon Bedrock.
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 now and migrate to Bedrock in a follow-up release once the team has more experience with the Bedrock API.

Question 2

Your Claude application produces good responses for typical inputs but struggles with edge cases. You have several labeled examples of edge-case inputs and the desired response for each. You want to use these examples to improve the model's handling of edge cases.
What is the best way to use these examples?

A. Tell users to avoid submitting the edge-case inputs to the application by adding warnings in the application's user interface.
B. Embed the examples in a database for the model to find during inference.
C. Train a custom model on the edge-case examples and deploy that custom model in place of the team's current Claude integration.
D. Add the labeled edge-case examples to the prompt as few-shot examples so the model can learn the pattern.

Question 3

You are writing a system prompt for a Claude application that needs to produce output in a specific JSON shape. The downstream system will reject any output that does not match the schema.
Your prompt would need to...

A. Omit any reference to the format and rely on a post-processing step in the application to reshape Claude's output.
B. Instruct Claude to use whichever output format it considers most appropriate for each request the application handles.
C. Include explicit constraints describing the required JSON schema and an instruction to produce only output matching that schema.
D. Instruct Claude to return JSON sometimes and free text other times so the application's output captures both formats.

Question 4

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. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
C. 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.
D. 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.

Question 5

Your Claude application has multi-step workflows where each step's output is needed only briefly before the agent moves on. The cumulative tool output is filling the context window with content that is no longer relevant.
How would you handle the accumulating tool output?

A. Switch to a smaller Claude model that processes context more efficiently and treat any quality loss as a tradeoff for the cost reduction.
B. Apply tool output pruning to remove tool outputs that are no longer needed by later steps in the workflow.
C. Apply prompt caching to the accumulated tool outputs so the application does not re-pay for the older content on each subsequent step.
D. Keep every tool output in the context indefinitely so the agent has the full record of every step it has executed during the workflow.

Solutions: