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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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

Question 2

A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?

A. Add detailed prompting that instructs the agent to track which sub-issues have been resolved and which remain.
B. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.
C. Increase the size of the agent's context window so it can hold the full ticket history at once.
D. Introduce an orchestrator agent that delegates billing, shipping, and product sub-issues to dedicated subagents.

Question 3

A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi- section system prompts heavily. Initial evaluation on the application's actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.
How would you decide?

A. Upgrade immediately, because the 8 percent reasoning improvement outweighs the 3 percent malformed output rate across the application's typical request distribution.
B. Upgrade and add a downstream validation step that catches the 3 percent malformed output before it reaches users, treating the validation step as the team's mitigation for the format change.
C. Adapt the application's system prompt to the new model's format expectations and re-evaluate, then upgrade only if the adapted prompt eliminates the malformed output while preserving the reasoning improvements.
D. Stay on the previous model permanently to avoid the malformed output rate and any future format changes that subsequent model releases might introduce in the application.

Question 4

A Claude application is producing outputs that drift away from the expected JSON format after several conversation turns. The first few turns produce correctly formatted output, but later turns gradually lose structure.
How would you address the drift?

A. Truncate every response to the first few characters, validate that the truncated output matches the expected JSON structure, and log any mismatches for review.
B. Identify the failure mode as format drift, examine how the conversation context evolves over turns, and address the drift through context management or output validation.
C. Restart the application after every turn and monitor whether the format remains consistent across subsequent interactions.
D. Switch to a smaller Claude model and re-test the application to determine whether the drift persists across conversation turns.

Question 5

Your Claude application has multi-step workflows where each step's output is needed only briefly before the agent moves on. The cumulative tool output is filling the context window with content that is no longer relevant.
How would you handle the accumulating tool output?

A. Switch to a smaller Claude model that processes context more efficiently and treat any quality loss as a tradeoff for the cost reduction.
B. Apply prompt caching to the accumulated tool outputs so the application does not re-pay for the older content on each subsequent step.
C. Apply tool output pruning to remove tool outputs that are no longer needed by later steps in the workflow.
D. Keep every tool output in the context indefinitely so the agent has the full record of every step it has executed during the workflow.

Solutions: