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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

You are choosing a Claude model for a high-volume classification task. Each classification is straightforward, latency requirements are tight, and per-request cost matters at scale.
Which model would you choose?

A. The largest, highest-capability Claude model, to maximize quality on every classification the application produces during normal operation across all requests.
B. A mid-tier Claude model selected by default, because mid-tier models balance quality and cost in a way the team can apply across most tasks.
C. A smaller, faster Claude model, because the task is straightforward and the workload prioritizes latency and per-request cost at scale.
D. Multiple Claude models in series, where each request runs through more than one model and the application combines the outputs into a final classification.

Question 2

You are building a Claude application that processes 10,000 customer emails overnight to extract structured data. The work is non-interactive, runs once daily, and has a flexible completion window of several hours.
Which Claude API would you use?

A. The Batch API, which is designed for non-interactive workloads with flexible completion windows.
B. The streaming responses API to process each email and return partial results to a database as the model generates them.
C. The real-time Messages API, processing the emails one at a time sequentially to ensure consistent ordering of results.
D. The real-time Messages API with concurrent requests to process the emails as fast as possible during the overnight window.

Question 3

You are deciding between Claude models for a task. The team has identified three relevant tradeoff dimensions: quality, latency, and cost.
The right model is the one that...

A. Fits the task's quality, latency, and cost requirements together, recognizing that improving one dimension typically affects the others.
B. Meets the task's cost target within a defined latency budget, with quality validated against a representative sample of inputs after the model is selected.
C. Meets the task's quality requirements at an acceptable latency, with cost reviewed separately once the quality and latency bar has been established.
D. Satisfies the task's latency requirement first, then is evaluated against quality and cost thresholds to confirm the selection is acceptable across all three dimensions.

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 is a bug that the team will fix in the next release of the application, then create a work ticket to fix the bug.
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 comes from network latency and that switching to a faster network connection will produce more consistent results across runs.
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

You are deciding between deploying a Claude-powered agent on Anthropic's hosted infrastructure or self- hosting under a "bring your own cloud" model in your own AWS account. The agent processes customer data subject to your enterprise's data residency policies, but the team wants to ship quickly and avoid managing infrastructure.
Which deployment model would you recommend?

A. Deploying on Anthropic-hosted infrastructure while the team drafts a request to update the enterprise data residency policy to accommodate hosted AI deployments.
B. Self-hosting under BYOC for an initial pilot, then evaluating whether to migrate to Anthropic-hosted infrastructure once the agent's data-handling patterns are better understood.
C. Self-hosting under BYOC to satisfy the data residency requirement, while working with the infrastructure team to reduce the operational overhead of managing the deployment.
D. Deploying on Anthropic-hosted infrastructure to meet the team's shipping timeline, and flagging the data residency requirement for a follow-up compliance review after launch.

Solutions: