試験CCAR-F トピック1 問題46 スレッド
Anthropic CCAR-Fのリアル試験問題集
問題 #: 46
トピック #: 1
問題 #: 46
トピック #: 1
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?
Your extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?
おすすめの解答：A 解答を投票する
Limited review capacity should be concentrated on records with the highest probability of semantic error.
Schema validation confirms that the response has the correct structure and data types; it does not establish that the extracted values are accurate. Ambiguous wording, contradictory passages, missing evidence, and model- reported uncertainty are direct indicators that an extraction requires human judgment.
Anthropic's reliability guidance recommends permitting Claude to express uncertainty, grounding factual outputs in source material, and validating critical information because hallucination-reduction methods do not eliminate errors completely. These principles support routing uncertain or evidentially conflicted records to reviewers rather than treating syntactically valid output as automatically trustworthy. ( https://docs.anthropic.
com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations ) Option B may waste reviewer capacity on clear, well-supported values while overlooking ambiguous errors in other fields. Option C is reactive: downstream acceptance does not guarantee semantic correctness, and silent errors may never produce processing failures. Option D provides an unbiased estimate of overall quality and should be retained as a secondary audit mechanism, but random selection is not the most efficient way to intercept risky records when only 5% can be reviewed.
In production, model confidence should not be treated as a calibrated probability by itself. It should be combined with objective signals such as contradictory source spans, missing citations, OCR quality, validation warnings, document type, and business impact.
Official references/topics: Human-in-the-Loop Review, Uncertainty Handling, Source Grounding, Risk- Based Escalation.
Schema validation confirms that the response has the correct structure and data types; it does not establish that the extracted values are accurate. Ambiguous wording, contradictory passages, missing evidence, and model- reported uncertainty are direct indicators that an extraction requires human judgment.
Anthropic's reliability guidance recommends permitting Claude to express uncertainty, grounding factual outputs in source material, and validating critical information because hallucination-reduction methods do not eliminate errors completely. These principles support routing uncertain or evidentially conflicted records to reviewers rather than treating syntactically valid output as automatically trustworthy. ( https://docs.anthropic.
com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations ) Option B may waste reviewer capacity on clear, well-supported values while overlooking ambiguous errors in other fields. Option C is reactive: downstream acceptance does not guarantee semantic correctness, and silent errors may never produce processing failures. Option D provides an unbiased estimate of overall quality and should be retained as a secondary audit mechanism, but random selection is not the most efficient way to intercept risky records when only 5% can be reviewed.
In production, model confidence should not be treated as a calibrated probability by itself. It should be combined with objective signals such as contradictory source spans, missing citations, OCR quality, validation warnings, document type, and business impact.
Official references/topics: Human-in-the-Loop Review, Uncertainty Handling, Source Grounding, Risk- Based Escalation.
水沢** 2026-07-24 06:34:52
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