Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 192 discussion

B and E are correct. Textract to extract text from files. Rekognition can also be used for text detection but after Rekognition - it's mentioned that Transcribe is used. Transcribe is used for Speech to Text. So that option D may not be valid.

no brainer: B,E

E: Amazon Textract & Amazon Comprehend Medical obviously do the job with least operational overhead. D can do this but it will be extra work and overhead. B for running SQL queries on S3 bucket directly without extra overhead.

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.

another mistake from the admin, should correct this one, because we all agree

Answer - BE Option D mentions using Amazon Rekognition and Amazon Transcribe Medical, which are primarily designed for image and audio analysis, respectively. While they can be part of a document processing pipeline, Amazon Textract and Amazon Comprehend Medical are more suitable for extracting structured information from documents, making option E a better choice.

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.

B and E are the correct answers. B is correct because storing the scanned documents in Amazon S3 provides highly scalable and durable storage. Amazon Athena allows running SQL queries directly against the data in S3 without needing to load the data into a database. E is correct because using Lambda functions triggered by uploads provides a serverless approach to automatically process each document. Amazon Textract and Comprehend Medical can extract text and medical information without needing to manage server

Amazon Comprehend Medical for image reading https://aws.amazon.com/comprehend/medical/ . Amazon Transcribe Medical for speech audio. Remove D. Keep E. A is meaningless, remove A (EC2). B use Amazon S3, Athena for querying, keep B. Conclusion combination B and E are correct answers.

AC wrong as involve EC2. Either one of DE are correct so that makes B correct. Now E is obvious answer if we have read AWS FAQs

Textract to extract the content and Athena to run sql queries on S3 data

From a DE/ML perspective Lambda + Textract + S3 + Athena is the best way to go

Transcribe is used. Transcribe is used for Speech to Text

B is correct because it suggests writing the document information to an Amazon S3 bucket, which provides scalable and durable object storage. Using Amazon Athena, the data can be queried using SQL, enabling efficient analysis. E is correct because it involves creating an AWS Lambda function triggered by new document uploads. Amazon Textract is used to convert the documents to raw text, and Amazon Comprehend Medical extracts relevant medical information from the text. A is incorrect because writing the document information to an Amazon EC2 instance with a MySQL database is not a scalable or efficient solution for analysis. C is incorrect because creating an Auto Scaling group of Amazon EC2 instances for processing scanned files and extracting information would introduce unnecessary complexity and management overhead. D is incorrect because using an EC2 instance with a MySQL database for storing document information is not the optimal solution for scalability and efficient analysis.

It states in the question that the written documents are scanned. They are converted into images after being scanned. Rekognition would be best to analyse images.

Options B & E are correct answers.

Why CD are marked as correct??