A medical company is using AWS to build a tool to recommend treatments for patients. The company has obtained health records and self-reported textual information in English from patients. The…

Question

Options

• AUse Amazon SageMaker to build a recurrent neural network (RNN) to summarize the data.
• BUse Amazon Comprehend Medical to summarize the data.
• CUse Amazon Kendra to create a quick-search tool to query the data.
• DUse the Amazon SageMaker Sequence-to-Sequence (seq2seq) algorithm to create a text

• A
  2% (1)
• B
  91% (53)
• C
  2% (1)
• D
  5% (3)

Explanation

Amazon Comprehend Medical is a purpose-built NLP service that extracts medical entities (diagnoses, medications, treatments, dosages) from unstructured health text with zero model training required - making it the clear winner for least development effort on health record data. Option A (SageMaker RNN) and Option D (SageMaker seq2seq) both require significant ML expertise: you'd need to collect training data, design, train, and tune a custom model, which is substantial development effort. Option C (Amazon Kendra) is a search/retrieval service - it helps users find documents but doesn't analyze or extract insights from patient data the way the question asks.

Memory tip: Think "Comprehend Medical = instant medical NLP." Whenever an AWS exam question involves extracting meaning from health text with minimal effort, Comprehend Medical is almost always the answer - it's the only AWS service specifically designed for medical language understanding out of the box.

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