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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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.

Question 2

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 3

Your Claude application returns confident-sounding answers, but occasionally those answers contain factual errors that downstream systems treat as ground truth. The team is concerned about the application's confidence-versus-accuracy gap.
How would you address the gap?

A. Reject every response the application produces until a manual accuracy review is conducted on each response by a human reviewer before any downstream system uses it.
B. Lower the model's temperature so the model's responses sound less confident and downstream systems are less likely to treat the responses as ground truth in normal operation.
C. Apply skepticism toward confident output by adding validation steps, sourcing requirements, or confidence calibration before treating outputs as ground truth.
D. Add a disclaimer to every output telling users to verify the accuracy of the output and treat the disclaimer as the primary mechanism for managing the confidence-versus-accuracy gap.

Question 4

Your agent makes 10 to 15 tool calls per task, and you have noticed it sometimes loses track of earlier results by the time it reaches later steps. The agent's context window is large enough to hold all the messages, but the relevant information appears to get buried as the conversation grows.
How would you address this?

A. Switch to a different agentic framework that advertises automatic context-window management as a built-in feature.
B. Apply a context-management pattern that summarizes or prunes older tool outputs while preserving the active task state.
C. Increase the context window further so all tool outputs from every prior step remain in full detail throughout the task.
D. Reduce the number of tool calls per task by combining several existing tools into larger, multi-purpose tools.

Question 5

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. 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.
B. Fits the task's quality, latency, and cost requirements together, recognizing that improving one dimension typically affects the others.
C. 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.
D. Meets the task's quality requirements at an acceptable latency, with cost reviewed separately once the quality and latency bar has been established.

Solutions: