AWS Certified Generative AI – Professional (AIP-C01) — Question 68

A financial services company is creating a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock to generate summaries of market activities. The application relies on a vector database that stores a small proprietary dataset that has a low index count. The application must perform similarity searches. The Amazon Bedrock model's responses must maximize accuracy and maintain high performance.
The company needs to configure the vector database and integrate it with the application.
Which solution will meet these requirements?

Answer options

• A. Launch an Amazon MemoryDB cluster and configure the index by using the Flat algorithm. Configure a horizontal scaling policy based on performance metrics.
• B. Launch an Amazon MemoryDB cluster and configure the index by using the Hierarchical Navigable Small World (HNSW) algorithm. Configure a vertical policy based on performance metrics.
• C. Launch an Amazon Aurora PostgresSQL cluster and configure the index by using the Inverted File with Flat Compression (IVFFlat) algorithm. Configure the instance class to scale to a larger size when the load increases.
• D. Launch an Amazon DocumentDB cluster that has an Inverted File with Flat Compression (IVFFlat) index and a high probe value. Configure connections to the cluster as a replica set Distribute reads to replica instances.

Correct answer: A

Explanation

The correct answer is A because using Amazon MemoryDB with the Flat algorithm is suitable for low index count datasets and provides efficient similarity searches. Options B and C introduce unnecessary complexity or are not optimized for the specific low-index environment, while D focuses on DocumentDB, which is not aligned with the requirements of the application.