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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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

Question 2

A teammate has asked how to extend Claude Code with a custom Skill that the team can invoke during sessions. The Skill consists of a set of instructions and a few support scripts the team wants Claude to be able to call when the Skill is loaded.
Where is the right place to define the Skill?

A. Define the Skill in a Skills directory recognized by Claude Code, where Claude can discover and load it during sessions for the team's repositories.
B. Define the Skill inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.
C. Define the Skill in a personal scratch directory on each developer's machine and load it manually before each Claude Code session that needs it.
D. Define the Skill as a long inline instruction at the top of every CLAUDE.md file in the team's repositories so Claude has access to it on every session.

Question 3

Your Claude application uses structured output that is consumed by downstream code. The team wants to handle malformed or unexpected output gracefully so it does not crash downstream systems.
The best choice for handling this issue would be to...

A. Silently drop any output that does not match expectations so downstream systems do not see the malformed output the application receives from the model.
B. Crash on any unexpected output and require manual recovery on affected requests the application handles during normal operation.
C. Use the output the model produces and leave schema enforcement to a later phase of production after the application is more complete.
D. Apply defensive parsing to handle unexpected fields, missing values, and type mismatches that the downstream code might otherwise crash on.

Question 4

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. Plan a refactoring pass to consolidate duplicated logic, separate concerns, and simplify control flow before adding new features.
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. 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.
D. Continue adding features and plan a refactoring pass after the next two release cycles when the team has more bandwidth for internal work.

Question 5

Your Claude application processes 50-page legal contracts and produces summaries with citation references back to the source. The team is debating whether to send each contract whole or split it into smaller pieces.
The contracts fit within Claude's context window. Initial testing shows that whole-document processing produces summaries with stronger cross-section reasoning but occasionally drifts on citation accuracy in later sections. Chunked processing produces stronger citation accuracy per chunk but loses cross-section reasoning.
The team has not decided which property matters more.
How would you guide the team's decision?

A. Assess the cost and latency implications of both approaches against the application's performance requirements before recommending which processing strategy to adopt.
B. Identify which property matters more for the application's actual use case and let that decision drive the chunking approach, then validate the choice against representative contracts.
C. Review the citation accuracy results from chunked processing and determine whether the loss of cross- section reasoning produces summaries that still meet the application's quality bar.
D. Examine the use cases where cross-section reasoning failures occur and assess whether whole- document processing meets the application's accuracy requirements across a representative sample of contracts.

Solutions: