Anthropic CCA-F

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

1. A retail organization observes that Claude occasionally gives different wording when summarizing identical customer reviews. The summaries remain accurate. What is the MOST likely explanation?

A) The API returned cached responses.
B) Large language models are probabilistic.
C) Claude is malfunctioning.
D) Retrieval failed.

2. Which factor MOST directly affects API cost?

A) Number of processed tokens
B) Internet speed
C) Screen resolution
D) CPU clock frequency

3. A legal department requires Claude to answer only when sufficient evidence exists in retrieved documents. What should Claude do if evidence is insufficient?

A) Invent a likely answer.
B) Increase creativity.
C) Clearly indicate insufficient information.
D) Search social media automatically.

4. Your document extraction tool uses ML models to extract invoice fields (vendor, amount, date).
The models return confidence scores (0.0-1.0) for each extracted field. In production, you observe: (1) the agent proceeds with low-confidence extractions that are incorrect 23% of the time, and (2) the agent requests unnecessary human review for 31% of extractions that were actually correct. How should you restructure the tool's output?

A) Return fields with their raw confidence scores and add detailed few-shot examples to your system prompt demonstrating how to interpret different confidence ranges and when to request human review.
B) Return fields with confidence scores, plus a request_review boolean computed using your tested confidence thresholds, along with a review_reasons array explaining which fields triggered review.
C) Return fields organized into verified and needs_verification objects based on confidence thresholds.
D) Compute an aggregate extraction_quality score across all fields and return it alongside the extracted values. Include a text summary describing the overall extraction reliability.

5. After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

A) Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
B) Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.
C) Pass the full accumulated context from all prior agents.
D) Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.

Solutions: