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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

The Claude application your team built has grown over six months, and the prompt-handling code has accumulated duplication and tangled control flow. The functionality is working, but new features are getting harder to add.
How would you address this?

A. Continue adding features and plan a refactoring pass after the next two release cycles when the team has more bandwidth for internal work.
B. Move all the prompt-handling code into a single large function to reduce the number of files developers have to navigate when reading the code.
C. Plan a refactoring pass to consolidate duplicated logic, separate concerns, and simplify control flow before adding new features.
D. Refactor the prompt-handling code in small increments as part of each new feature ticket, treating the cleanup as a side effect of feature work.

Question 2

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

Question 3

A teammate has submitted a pull request that adds a Claude-powered feature to your service. The code works, but the prompt and model selection are hard-coded inline, error handling is missing, and there are no tests for the integration.
What would you request during code review?

A. Approve the pull request as-is, on the grounds that the feature works in the happy path and the missing pieces can be added in follow-up commits.
B. Request changes that move prompt and model configuration to a configurable location, add error handling for Claude API failures, and add tests for the integration.
C. Request changes that move prompt and model configuration to a configurable location and add tests, treating the missing error handling as a follow-up release item.
D. Approve the pull request and add the missing pieces yourself in a follow-up commit so the teammate can move on to other work immediately.

Question 4

A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi- section system prompts heavily. Initial evaluation on the application's actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.
How would you decide?

A. Upgrade and add a downstream validation step that catches the 3 percent malformed output before it reaches users, treating the validation step as the team's mitigation for the format change.
B. Stay on the previous model permanently to avoid the malformed output rate and any future format changes that subsequent model releases might introduce in the application.
C. Adapt the application's system prompt to the new model's format expectations and re-evaluate, then upgrade only if the adapted prompt eliminates the malformed output while preserving the reasoning improvements.
D. Upgrade immediately, because the 8 percent reasoning improvement outweighs the 3 percent malformed output rate across the application's typical request distribution.

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. Embed the examples in a database for the model to find during inference.
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. Tell users to avoid submitting the edge-case inputs to the application by adding warnings in the application's user interface.
D. Add the labeled edge-case examples to the prompt as few-shot examples so the model can learn the pattern.

Solutions: