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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

You have just shipped a new Claude-powered application to production. The development phase is complete, and the system is now in active use by internal teams.
The next phase of work for this system is...

A. Running a formal post-deployment review that assesses the development phase before the team does any further work on the system.
B. Operating and maintaining the system, including monitoring, responding to issues, and planning evolution.
C. Retiring the system, since shipping is the final lifecycle stage for any application that reaches production.
D. Handing the system over to a separate operations team that will manage it independently of the development team going forward.

Question 2

The Anthropic API deprecated a request parameter that your Claude application uses in approximately 40 places across the codebase. The deprecation notice gives a six-month window before the parameter is removed and recommends a replacement parameter with slightly different semantics.
You would respond to the deprecation by...

A. Keeping the deprecated parameter in place while writing a wrapper function around it to insulate the rest of the codebase from the eventual change.
B. Adding regression tests to cover the parameter's behavior, then migrating call sites in batches that you validate against regression tests.
C. Migrating all 40 call sites in a single change near the removal date to ensure the deprecated parameter continues to work as long as possible.
D. Swapping all 40 call sites in a single change right away to prevent drawn-out migration work that will delay ongoing functioning.

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

Question 4

Your team uses several plugins across multiple Claude applications, and a recent plugin update introduced a regression. The team had not been tracking plugin versions, so the team cannot easily identify which version was previously working. How would you address this?

A. Upgrade every plugin to the latest version on a regular cadence to keep version drift small, on the grounds that drift contributes to regression risk.
B. Treat plugins as untrackable third-party code and rely on plugin authors to communicate breaking changes when they happen, with no internal version tracking.
C. Stop using all plugins until the team can rebuild equivalent functionality directly into the application code, treating plugin avoidance as a way to remove version-related risk.
D. Add explicit plugin version tracking to the project's configuration so the team can identify, pin, and upgrade plugin versions deliberately.

Question 5

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. Choose the largest, highest-capability Claude model, on the grounds that "best results" is most likely to mean highest quality.
D. Ask the product team to specify quality, latency, and cost targets, then select the model whose tradeoffs best fit those targets.

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