Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 65 discussion

The correct solution is C: Use Amazon Textract to extract the text from the reports. Use Amazon Comprehend Medical to identify the PHI from the extracted text. Option C: Using Amazon Textract to extract the text from the reports, and Amazon Comprehend Medical to identify the PHI from the extracted text, would be the most efficient solution as it would involve the least operational overhead. Textract is specifically designed for extracting text from documents, and Comprehend Medical is a fully managed service that can accurately identify PHI in medical text. This solution would require minimal maintenance and would not incur any additional costs beyond the usage fees for Textract and Comprehend Medical.

Textract is more suitable than Rekognition as it is build to scan text documents and Comprehend helps identify PHI.

Ans C - Textract to 'read' data; Comprehend to assess whether its PHI

Textract = Extract text from PDF/iamges Comprehend Medical = PHI ABD are wrong products for this requirement so won't achieve the results

Both Rekognition and Textract possess the ability to detect text within images, yet they are optimized for differing applications. Rekognition specializes in identifying text located spatially within an image, for instance, words displayed on street signs, t-shirts, or license plates. Its typical use cases encompass visual search, content filtering, deriving insights from content, among others. However, it's not the ideal choice for images containing more than 100 words, as this exceeds its limitation. On the other hand, Textract is tailored more towards processing documents and PDFs, offering a comprehensive suite for Optical Character Recognition (OCR). It proves useful in scenarios involving financial reports, medical records, receipts, ID documents, and more.

The correct solution is C: Use Amazon Textract to extract the text from the reports. Use Amazon Comprehend Medical to identify the PHI from the extracted text. Option C: Using Amazon Textract to extract the text from the reports, and Amazon Comprehend Medical to identify the PHI from the extracted text, would be the most efficient solution as it would involve the least operational overhead. Textract is specifically designed for extracting text from documents, and Comprehend Medical is a fully managed service that can accurately identify PHI in medical text. This solution would require minimal maintenance and would not incur any additional costs beyond the usage fees for Textract and Comprehend Medical.

• Amazon Textract: This program is made to extract text and data from scanned documents, such as pictures and PDFs. It helps to retain the formatting of the report by automatically extracting text while preserving the document's layout. Identifying and extracting medical information, including protected health information (PHI), from unstructured text is the specialty of Amazon Comprehend Medical. Medical entities that are frequently included in reporting on healthcare, such as ailments, drugs, and more, can be recognized by it.

with the choices here, I would go with C, but if offered, I would use amazon textract for the text and use Macie to do the scanning of text files, not comprehend.

Here's why: Amazon Textract has built-in support to extract text from PDFs and images, eliminating the need to build this yourself with Python libraries. Amazon Comprehend Medical has pre-trained machine learning models to identify PHI entities out-of-the-box, avoiding the need to train your own SageMaker model. Using these fully managed AWS services minimizes operational overhead of maintaining machine learning models yourself.

Option C is the right answer.

C leverages capabilities of Textract, which is a service that automatically extracts text and data from documents, including PDF and JPEG. By using Textract, hospital can extract text content from reports without need for additional custom code or libraries. Once text is extracted, hospital can then use Comprehend Medical, a natural language processing service specifically designed for medical text, to analyze and identify PHI. It can recognize medical entities such as medical conditions, treatments, and patient information. A. suggests using existing Python libraries, which would require hospital to develop and maintain custom code for text extraction and PHI identification. B and D involve using Textract along with SageMaker or Rekognition, respectively, for PHI identification. While these options could work, they introduce additional complexity by incorporating machine learning models and training.

Key word: hospital!

Answer C:

Selected Answer: C Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents.

Option C

WHY OPTION D IS WRONG

Agreed