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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:

1. Under which condition is a validation-retry loop fundamentally ineffective for improving data extraction quality?

A) When the required information is entirely absent from the provided source document.
B) When the model outputs a format mismatch.
C) When there is a structural output error in the response.
D) When the line items fail to sum to the stated total.

2. To extract metadata from a highly variable set of invoices, you use an enum field for document_type. How should you handle unexpected document types to maintain extraction quality?

A) Provide an enum containing only known types and rely on the model to find the closest match.
B) Include an other enum value paired with a detail string field for extensible categorization.
C) Implement a PreToolUse hook to drop unrecognized document types.
D) Omit the enum constraint entirely and use an open-ended string field.

3. When building the raw tool-handling conversation loop with the API/SDK, Claude may return a ToollJse block. To properly continue the multi-turn agentic loop, what are the requirements for formatting and returning the result of the executed function to Claude? Choose 2 correct answers.

A) The result must be passed back inside an assistant role message with an is_error'. false flag.
B) The result must be appended to the messages array inside a user role message containing a tool_result block.
C) The tool result must strictly be a serialized JSON object; raw text strings will automatically trigger a loop termination.
D) The tool result block must include a that matches the ID generated by Claude's initial tool request.

4. In a Continuous Integration (CI) pipeline, you run a script where a single Claude session generates a block of code and then immediately reviews its own changes in the same session. Why is this considered an architectural anti-pattern?

A) Same-session reviews trigger rate limiting automatically, breaking the CI/CD pipeline.
B) It exceeds the maximum token limit for the context window, causing the session to silently truncate the generated code.
C) The Message Batches API does not support multi-turn tool calling within a single request.
D) The reviewer retains the reasoning context from the code generation phase, creating confirmation bias and reducing the likelihood of catching subtle issues.

5. You are building a classifier to categorize customer reviews, but the model struggles with ambiguous scenarios like sarcasm and mixed sentiments. You decide to use few-shot prompting. What is the recommended best practice for this scenario?

A) Avoid few-shot prompting and instead use a detailed system prompt explaining the exact definition of sarcasm.
B) Provide 8 to 10 examples to ensure every possible sentiment category is covered comprehensively.
C) Provide exactly 1 example of a standard positive review to establish the JSON format.
D) Provide 2 to 4 examples, ensuring that at least one example specifically addresses an ambiguous edge case like sarcasm.

Solutions: