AWS Certified Generative AI Developer - Professional AIP-C01

By Amazon · Question Mode

QuestionQ8

Operational Efficiency and Optimization for GenAI Applications

A GenAI developer is developing a Retrieval Augmented Generation (RAG)-based customer-support application that uses Amazon Bedrock foundation models (FMs). The application must process 50 GB of historical customer conversations stored as JSON files in an Amazon S3 bucket. It must use the processed data as its retrieval corpus.

The application's data-processing workflow must extract relevant data from customer-support documents, remove customer personally identifiable information (PII), and generate embeddings for vector storage. The workflow must be cost-effective and complete within 4 hours.

Which solution meets these requirements with the LEAST operational overhead?

Explanation

AWS Step Functions can orchestrate managed service integrations for PII detection and embedding generation without operating compute fleets or custom processing infrastructure. Amazon Comprehend detects PII, Amazon Bedrock generates embeddings, and Amazon OpenSearch Serverless stores vectors and supports similarity search. This serverless pipeline minimizes operational administration while supporting scalable parallel processing.

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