A hospital is digitizing a large archive of historical written records and will continue to add hundreds of documents per day. The data team will scan documents and upload them to AWS. A solutions architect must analyze the documents, extract medical information, and store the results so an application can run SQL queries on the data. The solution should maximize scalability and minimize operational effort. Which two steps should the solutions architect take? (Choose two.)

The correct options leverage serverless services for scalability and minimal operational effort. Uploading documents to an S3 bucket is a highly scalable and cost-effective storage solution. Using a Lambda function triggered by S3 uploads allows for event-driven processing. Amazon Textract is ideal for extracting text and data from scanned documents, and Amazon Comprehend Medical is specifically designed to detect and extract protected health information (PHI) and medical entities from unstructured text. Finally, storing the extracted data in S3 and querying it with Amazon Athena provides a serverless, SQL-compatible analytics service. Incorrect options: Writing to an EC2 instance with MySQL introduces operational overhead and scaling limitations. Using Amazon Rekognition for text conversion is incorrect; Rekognition is for image and video analysis. Amazon Transcribe Medical is for speech-to-text, not document analysis. Creating an Auto Scaling group of EC2 instances for processing adds operational overhead compared to a serverless Lambda function.