AIP-C01 Question #44: Real Exam Question with Answer & Explanation

Question

A legal research company has a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock and Amazon OpenSearch Service. The application stores 768-dimensional vector embeddings for 15 million legal documents, including statutes, court rulings, and case summaries. The company's current chunking strategy segments text into fixed-length blocks of 500 tokens. The current chunking strategy often splits contextually linked information such as legal arguments, court opinions, or statute references across separate chunks. Researchers report that generated outputs frequently omit key context or cite outdated legal information. Recent application logs show a 40% increase in response times. The p95 latency metric exceeds 2 seconds. The company expects storage needs for the application to grow from 90 GB to 360 GB within a year. The company needs a solution to improve retrieval relevance and system performance at scale. Which solution will meet these requirements?

Options

• AIncrease the embedding vector dimensionality from 768 to 4,096 without changing the existing
• BReplace dynamic retrieval with static, pre-written summaries that are stored in Amazon S3. Use
• CUpdate the chunking strategy to use semantic boundaries such as complete legal arguments,
• DMigrate from OpenSearch Service to Amazon DynamoDB. Implement keyword-based indexes to