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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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. Increase the size of the agent's context window so it can hold the full ticket history at once.
C. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.
D. Introduce an orchestrator agent that delegates billing, shipping, and product sub-issues to dedicated subagents.

Question 2

Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?

A. Remove the system prompt entirely to make room for longer transcripts in each request, freeing up context window space the system prompt would otherwise consume.
B. Increase the temperature parameter as the junior developer suggested and observe whether the context window issue resolves over the next several runs of the application in production.
C. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
D. Adjust the temperature parameter together with the max_tokens parameter, treating the combined adjustment as the team's mechanism for managing context window pressure during long-transcript processing.

Question 3

Your Claude application uses tool calling to fetch patient data and generate summary reports. The flow occasionally fails because the model returns a tool_use block that references arguments not present in the schema, and your application code does not handle this case gracefully.
How would you address this?

A. Stop using tool calling entirely and replace tools with prompted text generation that asks the model to describe what it would do.
B. Retry the same request repeatedly until the model returns a valid tool_use block that matches the schema as expected.
C. Validate the tool_use block's arguments against the tool schema before dispatching the tool and handle invalid arguments as a recognized error path.
D. Log invalid tool_use blocks when they occur and allow the tool dispatch to proceed, relying on the tool's own error handling to surface failures back to the application.

Question 4

Your Claude application has been running for several conversation turns, and you notice the model occasionally references information that was discussed many turns ago but is no longer relevant. You suspect context drift is causing the model to weight stale content too heavily.
How would you address the drift?

A. Truncate the conversation so the model sees only the most recent turn during each subsequent response.
B. Increase the context window size so all turns of the conversation remain visible to the model in full detail.
C. Reset the conversation after every turn so the model loses all prior turns when generating a response.
D. Apply compaction to summarize older portions of the conversation so the gist remains while the specifics carry less weight.

Question 5

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. Swapping all 40 call sites in a single change right away to prevent drawn-out migration work that will delay ongoing functioning.
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. Keeping the deprecated parameter in place while writing a wrapper function around it to insulate the rest of the codebase from the eventual change.

Solutions: