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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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

Question 2

Your Claude application's error handling currently logs every API error with the same severity level. The team wants to differentiate between errors that should page an on-call engineer and errors that should be logged for later review. How would you structure the error handling?

A. Categorize errors by severity based on impact and recoverability, then route each category to the appropriate channel for paging or logging.
B. Page on every error, on the grounds that paging guarantees that no error is missed by the team during normal operation across the application's lifecycle.
C. Log every error with the same severity, on the grounds that differentiating severity adds complexity that does not pay off in most application setups over time.
D. Disable logging for any error that does not page, treating non-paging errors as not worth recording for later review either.

Question 3

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

Question 4

You are setting up a CI/CD pipeline for a new Claude application. The pipeline needs to run automated checks on every pull request before code can be merged.
The CI/CD checks would include...

A. Automated tests of the Claude integration, linting, and any other standard quality gates the team applies to its other services.
B. Automated tests of the Claude integration only, with linting handled separately during local development on each developer's machine.
C. Automated linting and security scanning, with Claude integration testing handled manually during pre- release verification by a designated reviewer.
D. A full end-to-end production deployment on every pull request to catch all possible issues before any code is merged into the main branch.

Question 5

Your Claude agent has too many tools, and many of them have overlapping functionality. The agent often picks an inappropriate tool when several could plausibly handle a request.
How would you address the tool selection problem?

A. Restructure the tool set by consolidating overlapping tools, removing unused tools, and clarifying tool descriptions so each tool has a distinct purpose.
B. Remove all tools and rely on the agent's general capability instead, with the application losing the workflows that previously relied on tools.
C. Add more tools to cover every variation of the requests the agent handles, on the grounds that more tools give the agent more accurate options to choose from.
D. Add detailed examples to each tool's description so the agent can match incoming requests to the right tool by example, treating the examples as the team's selection mechanism.

Solutions: