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Answer: Write the document information to an Amazon S3 bucket. Use Amazon Athena to query the data., Create an AWS Lambda function that runs when new documents are uploaded. Use Amazon Textract to convert the documents to raw text. Use Amazon Comprehend Medical to detect and extract relevant medical information from the text.
## Explanation **Correct Answers: B and E** **Why B is correct:** - **Amazon S3** provides highly scalable, durable, and cost-effective storage for large volumes of documents - **Amazon Athena** allows running SQL queries directly on data stored in S3 without needing to manage infrastructure - This combination provides serverless, scalable querying capability that meets the requirement to "run SQL queries on the data" **Why E is correct:** - **AWS Lambda** provides serverless, event-driven processing that automatically scales with the volume of documents - **Amazon Textract** is specifically designed for extracting text and data from scanned documents (not Amazon Rekognition, which is for image/video analysis) - **Amazon Comprehend Medical** is specifically designed to extract medical information from text (not Amazon Transcribe Medical, which is for speech-to-text) - This combination maximizes scalability and operational efficiency by using serverless services **Why other options are incorrect:** **A (EC2 with MySQL):** - EC2 instances require manual scaling and management - MySQL on EC2 doesn't provide the same scalability as serverless solutions - Operational overhead is higher compared to serverless options **C (Auto Scaling EC2 with custom application):** - Requires managing EC2 instances and scaling policies - Custom application development and maintenance adds complexity - Less operationally efficient than serverless solutions **D (Lambda with Rekognition and Transcribe Medical):** - Amazon Rekognition is for image/video analysis, not document text extraction - Amazon Transcribe Medical is for converting speech to text, not processing written documents - Wrong combination of services for the use case **Key AWS Services Used:** 1. **AWS Lambda** - Serverless compute for event-driven processing 2. **Amazon Textract** - OCR service for extracting text from scanned documents 3. **Amazon Comprehend Medical** - NLP service for extracting medical information 4. **Amazon S3** - Scalable object storage 5. **Amazon Athena** - Serverless SQL query service for S3 data This solution provides maximum scalability (serverless services auto-scale) and operational efficiency (no infrastructure to manage).
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A hospital wants to create digital copies for its large collection of historical written records. The hospital will continue to add hundreds of new documents each day. The hospital's data team will scan the documents and will upload the documents to the AWS Cloud.
A solutions architect must implement a solution to analyze the documents, extract the medical information, and store the documents so that an application can run SQL queries on the data. The solution must maximize scalability and operational efficiency.
Which combination of steps should the solutions architect take to meet these requirements? (Choose two.)
A
Write the document information to an Amazon EC2 instance that runs a MySQL database.
B
Write the document information to an Amazon S3 bucket. Use Amazon Athena to query the data.
C
Create an Auto Scaling group of Amazon EC2 instances to run a custom application that processes the scanned files and extracts the medical information.
D
Create an AWS Lambda function that runs when new documents are uploaded. Use Amazon Rekognition to convert the documents to raw text. Use Amazon Transcribe Medical to detect and extract relevant medical information from the text.
E
Create an AWS Lambda function that runs when new documents are uploaded. Use Amazon Textract to convert the documents to raw text. Use Amazon Comprehend Medical to detect and extract relevant medical information from the text.