Anthropic CCDV-F : Claude Certified Developer-Foundations

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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question #1
You are setting up the configuration management approach for a new Claude Code project. Your team will use CLAUDE.md files and settings.json files to control behavior, and you want to make sure changes are tracked and reviewable.
The configuration management approach would...

A. Version-control CLAUDE.md and settings.json files in a separate repository from the project's source code, so configuration evolves independently from the application code over time.
B. Version-control CLAUDE.md and settings.json files alongside the project's source code, with changes reviewed through standard pull request workflows the team applies.
C. Version-control CLAUDE.md alongside the project's source code and settings.json files in a separate repository from the project's source code.
D. Duplicate CLAUDE.md and settings.json files in multiple repositories to provide redundancy, on the grounds that a single source of truth is risky for project configuration.

Question #2
Your enterprise has a contract with AWS that requires Claude API calls to flow through Amazon Bedrock rather than the direct Anthropic API. Your team is building a new Claude application and is unfamiliar with this constraint.
How would you build the application?

A. Build two parallel implementations of every call, one for the direct Anthropic API and one for Bedrock, and pick the faster one at runtime.
B. Build the application against the direct Anthropic API now and migrate to Bedrock in a follow-up release once the team has more experience with the Bedrock API.
C. Configure the application to invoke Claude through the Bedrock-compatible API path while keeping the application's logic provider-agnostic.
D. Build the application against the direct Anthropic API and ignore the contractual requirement to route Claude calls through Amazon Bedrock.

Question #3
Your Claude application is producing inconsistent outputs for similar inputs, even when using the same model and prompt. You want to debug the issue systematically.
Your debugging approach would...

A. Assume inconsistent outputs reflect normal LLM non-determinism and document the variability as an accepted characteristic of the application's behavior.
B. Capture full traces of input, system prompt, user messages, model output, and parameters, then analyze the differences between consistent and inconsistent runs.
C. Retry every request that produces an unexpected output and log the results to identify whether repeated calls converge on a consistent response pattern.
D. Lower the model's temperature and re-run the inconsistent inputs to determine whether the parameter change reduces output variability across runs.

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

Question #5
Your team's Claude agent has accumulated several customizations that bypass the SDK's defaults, including custom history management, retry logic, and error handling. A new team member has proposed reverting all the customizations to maintain the codebase more easily. The tech lead disagrees and says each customization was added for a reason.
How would you advise the team?

A. Revert all customizations to the SDK's defaults to standardize the codebase.
B. Keep all customizations, trusting that the tech lead's original reasoning is still valid.
C. Migrate the agent off the SDK and rebuild it with a custom loop.
D. Decide on each customization individually based on its original reason and the SDK's current capabilities.

Solutions: