Anthropic CCDV-F Exam : Claude Certified Developer-Foundations

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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

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. 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.
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. 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.
D. Upgrade immediately, because the 8 percent reasoning improvement outweighs the 3 percent malformed output rate across the application's typical request distribution.

Question 2

Your Claude application's token costs have grown faster than expected. The team has not been tracking token usage by feature, so the team cannot identify which features are driving cost. The team is debating how to respond.
How would you respond?

A. Switch every feature to the smallest model to cut cost broadly across the application during normal operation.
B. Tell the team that cost growth is unavoidable as the application scales and that no investigation will change the trajectory of the application's cost over the next several quarters.
C. Reduce token usage uniformly across all features by half, applying the cut evenly across the application during normal operation.
D. Add token usage tracking by feature to the application's logging so the team can identify which features drive cost before recommending changes.

Question 3

You have just shipped a new Claude-powered application to production. The development phase is complete, and the system is now in active use by internal teams.
The next phase of work for this system is...

A. Running a formal post-deployment review that assesses the development phase before the team does any further work on the system.
B. Retiring the system, since shipping is the final lifecycle stage for any application that reaches production.
C. Handing the system over to a separate operations team that will manage it independently of the development team going forward.
D. Operating and maintaining the system, including monitoring, responding to issues, and planning evolution.

Question 4

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

Question 5

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

Solutions: