Study with CCDV-F Actual Test Questions

Anthropic CCDV-F Exam Overview:

Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude application uses tool calling to fetch patient data and generate summary reports. The flow occasionally fails because the model returns a tool_use block that references arguments not present in the schema, and your application code does not handle this case gracefully.
How would you address this?

A. Validate the tool_use block's arguments against the tool schema before dispatching the tool and handle invalid arguments as a recognized error path.
B. Retry the same request repeatedly until the model returns a valid tool_use block that matches the schema as expected.
C. Stop using tool calling entirely and replace tools with prompted text generation that asks the model to describe what it would do.
D. Log invalid tool_use blocks when they occur and allow the tool dispatch to proceed, relying on the tool's own error handling to surface failures back to the application.

Question 2

A teammate has asked you to explain the difference between context engineering and prompt engineering.
They have heard the terms used interchangeably and are unsure how each applies to a Claude application that processes long-running multi-step tasks.
How would you describe the distinction?

A. Prompt engineering shapes individual prompts for specific outputs, while context engineering manages how content flows across turns and steps and takes steps to keep relevant state visible.
B. Prompt engineering and context engineering each address content the team gives Claude, but the team can group them under a single workflow because the practices use overlapping techniques.
C. Prompt engineering is the older term for prompt design, while context engineering is the newer term that has replaced it in modern Claude applications across the industry.
D. Prompt engineering focuses on the model's response, while context engineering focuses on the user's input across many sessions in a long-running multi-step Claude application.

Question 3

Your Claude application is producing inconsistent outputs for similar inputs, even when using the same model and prompt. You want to debug the issue systematically.
Your debugging approach would...

A. Retry every request that produces an unexpected output and log the results to identify whether repeated calls converge on a consistent response pattern.
B. Lower the model's temperature and re-run the inconsistent inputs to determine whether the parameter change reduces output variability across runs.
C. Assume inconsistent outputs reflect normal LLM non-determinism and document the variability as an accepted characteristic of the application's behavior.
D. Capture full traces of input, system prompt, user messages, model output, and parameters, then analyze the differences between consistent and inconsistent runs.

Question 4

A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?

A. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.
B. Introduce an orchestrator agent that delegates billing, shipping, and product sub-issues to dedicated subagents.
C. Add detailed prompting that instructs the agent to track which sub-issues have been resolved and which remain.
D. Increase the size of the agent's context window so it can hold the full ticket history at once.

Question 5

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. Train a custom model on the edge-case examples and deploy that custom model in place of the team's current Claude integration.
C. Embed the examples in a database for the model to find during inference.
D. Add the labeled edge-case examples to the prompt as few-shot examples so the model can learn the pattern.

Solutions: