AWS Certified Generative AI – Professional (AIP-C01) — Question 8

A medical company is building a generative AI (GenAI) application that uses RAG to provide evidence-based medical information. The application uses Amazon OpenSearch Service to retrieve vector embeddings. Users report that searches frequently miss results that contain exact medical terms and acronyms and return too many semantically similar but irrelevant documents. The company needs to improve retrieval quality and maintain low end user latency, even as the document collection grows to millions of documents.
Which solution will meet these requirements with the LEAST operational overhead?

Answer options

• A. Configure hybrid search by combining vector similarity with keyword matching to improve semantic understanding and exact term and acronym matching.
• B. Increase the dimensions of the vector embeddings from 384 to 1536. Use a post-processing AWS Lambda function to filter out irrelevant results after retrieval.
• C. Replace OpenSearch Service with Amazon Kendra. Use query expansion to handle medical acronyms and terminology variants during pre-processing.
• D. Implement a two-stage retrieval architecture in which initial vector search results are re-ranked by an ML model that is hosted on Amazon SageMaker AI.

Correct answer: A

Explanation

Option A is correct as it combines vector similarity with keyword matching, effectively improving both semantic understanding and the accuracy of exact term and acronym matching, which addresses the user's concerns. Option B increases vector dimensions but does not solve the issue of irrelevant results effectively. Option C involves replacing the search service, which increases operational overhead and may not guarantee better outcomes. Option D suggests a complex architecture that may complicate operations without addressing the issue as directly as option A.