AIP-C01 Question #31: Real Exam Question with Answer & Explanation

Question

A company uses Amazon Bedrock to build a Retrieval Augmented Generation (RAG) system. The RAG system uses an Amazon Bedrock Knowledge Bases that is based on an Amazon S3 bucket as the data source for emergency news video content. The system retrieves transcripts, archived reports, and related documents from the S3 bucket. The RAG system uses state-of-the-art embedding models and a high-performing retrieval setup. However, users report slow responses and irrelevant results, which cause decreased user satisfaction. The company notices that vector searches are evaluating too many documents across too many content types and over long periods of time. The company determines that the underlying models will not benefit from additional fine-tuning. The company must improve retrieval accuracy by applying smarter constraints and wants a solution that requires minimal changes to the existing architecture. Which solution will meet these requirements?

Options

• AEnhance embeddings by using a domain-adapted model that is specifically trained on emergency
• BMigrate to Amazon OpenSearch Service. Use vector fields and metadata filters to define the
• CEnable metadata-aware filtering within the Amazon Bedrock knowledge base by indexing S3
• DMigrate to an Amazon Q Business index to perform structured metadata filtering and document