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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

The team is debating whether to integrate with the Claude API directly or through a third-party abstraction layer that supports multiple LLM providers. The team has identified that all current and projected use cases run on Claude, no internal customer has requested LLM portability, and the team's product roadmap does not mention multi-provider support over the next two years. The third-party abstraction would add roughly 15 percent overhead in code complexity and introduce one additional dependency.
Which integration approach would you recommend?

A. A custom multi-provider abstraction layer the team builds in-house so that the team controls every part of the abstraction the application uses for its API calls.
B. Both integration paths in parallel, where the application uses each path on different runs to compare which performs better in production over time.
C. The third-party abstraction layer, on the grounds that multi-provider support is valuable for any application as a matter of long-term flexibility across vendors.
D. Direct integration with Claude through its SDK, because no multi-provider need exists and abstraction would add complexity that does not pay off.

Question 2

You are designing an agent that handles a multi-step research task. You want the agent to break the task into smaller pieces, hand each piece to a focused subagent, and consolidate the results.
The agent pattern you would apply is...

A. A memory pattern that stores the entire research history in advance, before any subtask begins execution.
B. An orchestrator and subagent pattern with specialized subagents assigned to each subtask.
C. A single tool-use loop that includes every tool the agent might need across all subtasks.
D. A context-window pruning pattern that drops each subtask's content after the agent moves on.

Question 3

Your Claude application receives untrusted input from external sources. The team is establishing how the application should treat this untrusted input.
Untrusted input would be...

A. Validated and sanitized before being incorporated into prompts, and treated as data the model should not interpret as instructions.
B. Treated identically to trusted input from internal sources, on the grounds that all input the application receives can be handled the same way.
C. Blocked entirely so the application accepts only trusted input, with no path for legitimate external input the application might process.
D. Routed through a separate Claude application with more lax security controls so the original application does not handle the untrusted input directly.

Question 4

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

Question 5

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. Switch to a smaller Claude model and re-test the application to determine whether the drift persists across conversation turns.
B. Restart the application after every turn and monitor whether the format remains consistent across subsequent interactions.
C. 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.
D. Truncate every response to the first few characters, validate that the truncated output matches the expected JSON structure, and log any mismatches for review.

Solutions: