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Answer: Store documents in an Amazon S3 bucket and employ Amazon Athena for data querying., Implement a Lambda function activated by document uploads, using Amazon Textract for text conversion and Amazon Comprehend Medical for extracting pertinent medical insights.
The correct steps to meet the hospital's requirements are B and E. Option B recommends storing documents in an Amazon S3 bucket and using Amazon Athena for querying the data, which maximizes scalability and operational efficiency as S3 is a highly scalable storage service, and Athena provides serverless SQL querying capabilities. Option E suggests creating an AWS Lambda function that triggers on document uploads, using Amazon Textract to convert the documents to raw text and Amazon Comprehend Medical to extract relevant medical information. This approach leverages fully managed, scalable AWS services specifically designed for text extraction and medical information processing.
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A hospital seeks to digitize its extensive historical records, with daily additions of hundreds of new documents. The scanned documents will be uploaded to AWS. The architect's task is to implement a scalable, efficient solution for analyzing, extracting medical data, and enabling SQL query capability on the stored documents. Which two-step combination best meets these criteria?
A
Utilize an Amazon EC2 instance with a MySQL database for document storage.
B
Store documents in an Amazon S3 bucket and employ Amazon Athena for data querying.
C
Establish an Auto Scaling group of EC2 instances to execute a custom application for processing and medical data extraction from scanned files.
D
Deploy a Lambda function triggered by document uploads, leveraging Amazon Rekognition for text conversion and Amazon Transcribe Medical for medical information extraction.
E
Implement a Lambda function activated by document uploads, using Amazon Textract for text conversion and Amazon Comprehend Medical for extracting pertinent medical insights.
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