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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

You are implementing a custom tool for your Claude agent. The tool needs to interact with an external pricing service that returns product data.
Which of the following best practices would you apply as you develop this tool?

A. Define the tool with a loose schema and let the model interpret the inputs flexibly on each call the agent makes.
B. Omit the tool description and let the model infer when to use the tool based on the tool's name and the rest of the prompt context.
C. Define the tool with a clear schema, write a precise description for when to call it, and handle pricing service errors explicitly.
D. Implement the tool with no error handling and let the agent loop catch failures whenever the pricing service returns an error during operation.

Question 2

You are designing a Claude application that helps medical researchers analyze multi-step clinical case studies.
The application must work through differential diagnoses by considering symptom patterns, weighing evidence across competing hypotheses, and showing intermediate reasoning steps before producing a final recommendation. The team is choosing among Claude's available model options.
The model option best suited to this use case is...

A. Fast mode, which prioritizes the lowest possible latency at the expense of reasoning depth on complex tasks.
B. Extended thinking, which lets the model reason through the differential diagnosis steps before producing the final recommendation.
C. A smaller model with a tighter context window, which encourages the model to focus its limited capacity on the task.
D. Zero-shot prompting alone with no model option adjustments, which keeps the application's configuration as simple as possible.

Question 3

You maintain a Claude application that uses Claude Sonnet 4.5 across several production workflows.
Anthropic released Claude Sonnet 4.7, which your evaluation suite shows performing 8% better on your highest-volume task. However, this version produces different output formatting on two of your structured- extraction prompts that downstream consumers parse with regex-based code.
To roll out the upgrade, you would...

A. Switch the model identifier in the application's configuration to the new model, then monitor production for parsing failures to be fixed as failures surface.
B. Rewrite the downstream parsing code to accept a wider range of output formats to ensure the application is resilient to future model upgrades without requiring prompt changes.
C. Keep the older model in production to preserve the fragile parsing layer that may risk breaking with any model change.
D. Adjust the prompts to constrain output format, re-run evaluations against the expected parsing-layer schema, then roll the model out with a feature flag and the option to revert per workflow.

Question 4

Your team is debating how to manage the prompts used in your Claude application. Some prompts are checked into the code repository, some live in a separate configuration file, and some are constructed inline at runtime. The result is inconsistent, and a recent prompt change went out without code review.
What steps would you take?

A. Move all prompts to inline runtime construction so the team can update them quickly through a streamlined process outside the standard code review workflow.
B. Establish a single source of truth for prompts, version-control them alongside code, and require code review for prompt changes.
C. Establish a single source of truth for prompts but keep change review optional, allowing developers to update prompts directly when changes are urgent.
D. Move all prompts out of version control to a separate spreadsheet that team members can edit freely as the application evolves over time.

Question 5

You are starting a new Claude application and have a small set of well-labeled examples that demonstrate the desired output format. You want to use these examples to guide Claude's behavior.
How would you guide the application's behavior?

A. Use multi-shot prompting by including the labeled examples in the prompt so Claude can match the desired output format on each request.
B. Train a custom model on the labeled examples before deployment so that the application does not need to include the examples in any prompt during operation.
C. Embed the examples in a database and retrieve them at runtime as reference material for the team.
D. Use zero-shot prompting and rely on the model's general capability to produce the desired output format consistently across all incoming requests.

Solutions: