Câu 16: CCA-F: Claude Certified Architect - Foundations

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?

1. 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.
2. 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. — đáp án hiện tại
3. C. 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.
4. D. Return fields organized into verified and needs_verification objects based on confidence thresholds.