AWS Certified Generative AI – Professional (AIP-C01) — Question 58

A financial services company needs to pre-process unstructured data such as customer transcripts, financial reports, and documentation. The company stores the unstructured data in Amazon S3 to support an Amazon Bedrock application.
The company must validate data quality, create auditable metadata, monitor data metrics, and customize text chunking to optimize foundation model (FM) performance.
Which solution will meet these requirements with the LEAST development effort?

Answer options

• A. Use Amazon SageMaker Data Wrangler to create a data flow. Configure Amazon CloudWatch metrics and alarms to monitor data quality. Use a custom AWS Lambda function to pre-process the data. Load processed data into Amazon Bedrock.
• B. Set up an AWS Glue crawler to catalog data sources. Create AWS Glue ETL jobs to run custom transformation scripts. Use AWS Glue Data Quality to validate and monitor data quality. Load processed data into Amazon Bedrock.
• C. Use Amazon Comprehend to extract entities. Create an AWS Lambda function to chunk text. Run Amazon Athena to query and validate data quality. Load processed data into Amazon Bedrock.
• D. Create an AWS Step Functions workflow to orchestrate data pre-processing tasks. Run custom code on Amazon EC2 instances to process the data. Use Amazon SageMaker Model Monitor to monitor data quality. Load processed data into Amazon Bedrock.

Correct answer: B

Explanation

Option B is the best choice as it efficiently utilizes AWS Glue to catalog and transform the data while also ensuring data quality, all of which align with the company's requirements with minimal development effort. Options A, C, and D involve more complex setups and custom coding that would require additional development time and resources.