AWS Certified Solutions Architect – Professional (SAP-C02) — Question 91

A company has migrated Its forms-processing application to AWS. When users interact with the application, they upload scanned forms as files through a web application. A database stores user metadata and references to files that are stored in Amazon S3. The web application runs on Amazon EC2 instances and an Amazon RDS for PostgreSQL database.

When forms are uploaded, the application sends notifications to a team through Amazon Simple Notification Service (Amazon SNS). A team member then logs in and processes each form. The team member performs data validation on the form and extracts relevant data before entering the information into another system that uses an API.

A solutions architect needs to automate the manual processing of the forms. The solution must provide accurate form extraction. minimize time to market, and minimize tong-term operational overhead.

Which solution will meet these requirements?

Answer options

• A. Develop custom libraries to perform optical character recognition (OCR) on the forms. Deploy the libraries to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster as an application tier. Use this tier to process the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data into an Amazon DynamoDB table. Submit the data to the target system's APL. Host the new application tier on EC2 instances.
• B. Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use artificial intelligence and machine learning (AI/ML) models that are trained and hosted on an EC2 instance to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.
• C. Host a new application tier on EC2 instances. Use this tier to call endpoints that host artificial intelligence and machine teaming (AI/ML) models that are trained and hosted in Amazon SageMaker to perform optical character recognition (OCR) on the forms. Store the output in Amazon ElastiCache. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.
• D. Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use Amazon Textract and Amazon Comprehend to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API.

Correct answer: D

Explanation

Option D is correct because it utilizes AWS services specifically designed for OCR and text analysis, ensuring accurate form extraction while leveraging serverless architecture to reduce operational overhead. Options A and C require custom development and infrastructure management, resulting in increased complexity and longer time to market. Option B, while it uses AI/ML, does not utilize the optimal services available for OCR, making it less effective than Option D.