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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude application's outputs are inconsistent in format. The team's instructions are scattered across the system prompt and user messages, with some instructions stated only once and others repeated.
How would you fix the inconsistency?

A. Move all instructions into the user message so the application's behavior is controlled at the input point.
B. Repeat all instructions in both the system prompt and the user message so the model sees each instruction more than once.
C. Consolidate behavioral and format instructions in the system prompt and keep user messages focused on the user's input.
D. Place each instruction wherever the developer who first wrote it found most natural, retaining the original intent.

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. A smaller model with a tighter context window, which encourages the model to focus its limited capacity on the task.
C. Extended thinking, which lets the model reason through the differential diagnosis steps before producing the final recommendation.
D. Zero-shot prompting alone with no model option adjustments, which keeps the application's configuration as simple as possible.

Question 3

A teammate is reviewing the team's threat model for a Claude application and has asked you to identify the categories of AI-specific threats that the model should cover. The teammate has already listed traditional web application threats and wants to know what additional categories apply to a Claude application.
Which AI-specific threat categories would you add?

A. Supply chain attacks on the Claude SDK because the SDK itself is the only point of vulnerability that a Claude application introduces beyond traditional web application threats.
B. Network-level denial of service and physical infrastructure attacks, because these categories cover the threats most likely to affect any Claude application in production.
C. Cross-site scripting and SQL injection, because these traditional web application threats apply with equal weight to any application that uses Claude in any way.
D. Prompt injection, data leakage from prompts or context, jailbreak attempts, and unsafe model output that bypasses application controls.

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. 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.
B. Automated tests of the Claude integration, linting, and any other standard quality gates the team applies to its other services.
C. Automated linting and security scanning, with Claude integration testing handled manually during pre- release verification by a designated reviewer.
D. Automated tests of the Claude integration only, with linting handled separately during local development on each developer's machine.

Question 5

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

Solutions: