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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

You are designing a Claude application that helps medical researchers analyze multi-step clinical case studies.
The application must work through differential diagnoses by considering symptom patterns, weighing evidence across competing hypotheses, and showing intermediate reasoning steps before producing a final recommendation. The team is choosing among Claude's available model options.
The model option best suited to this use case is...

A. Extended thinking, which lets the model reason through the differential diagnosis steps before producing the final recommendation.
B. Fast mode, which prioritizes the lowest possible latency at the expense of reasoning depth on complex tasks.
C. Zero-shot prompting alone with no model option adjustments, which keeps the application's configuration as simple as possible.
D. A smaller model with a tighter context window, which encourages the model to focus its limited capacity on the task.

Question 2

Your team is debating how to manage the prompts used in your Claude application. Some prompts are checked into the code repository, some live in a separate configuration file, and some are constructed inline at runtime. The result is inconsistent, and a recent prompt change went out without code review.
What steps would you take?

A. Move all prompts to inline runtime construction so the team can update them quickly through a streamlined process outside the standard code review workflow.
B. Establish a single source of truth for prompts but keep change review optional, allowing developers to update prompts directly when changes are urgent.
C. Move all prompts out of version control to a separate spreadsheet that team members can edit freely as the application evolves over time.
D. Establish a single source of truth for prompts, version-control them alongside code, and require code review for prompt changes.

Question 3

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

Question 4

You are explaining to a stakeholder why running the same Claude prompt twice can produce slightly different results. The stakeholder is concerned this means the application is broken.
How would you address the stakeholder's concern?

A. Tell the stakeholder the variation comes from network latency and that switching to a faster network connection will produce more consistent results across runs.
B. Explain that LLMs are non-deterministic by default due to sampling, and describe how the application handles this through validation, retries, or temperature adjustment.
C. Tell the stakeholder the variation is a bug that the team will fix in the next release of the application, then create a work ticket to fix the bug.
D. Tell the stakeholder the variation is caused by Claude being updated continuously by Anthropic, and that switching to a fixed model snapshot will eliminate the variation entirely.

Question 5

A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?

A. Tell the teammate that the variation is caused by the application sending two different prompts on different runs and propose a code change that pins the system prompt to a single version.
B. Confirm that the variation is a bug in the application's request construction and start investigating which part of the application is producing the inconsistent prompt text.
C. Tell the teammate that the variation depends on which Claude model serves the request, and recommend pinning the application to a single model version to make the output behavior consistent.
D. Explain that LLMs generate output token by token, and variation in how prompt content appears in output is a property of generation, not a bug in request construction.

Solutions: