AIP-C01 Question #81: Real Exam Question with Answer & Explanation

Question

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 with 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?

Options

• ALaunch an Amazon MemoryDB cluster and configure the index by using the Flat algorithm.
• BLaunch an Amazon MemoryDB cluster and configure the index by using the Hierarchical
• CLaunch an Amazon Aurora PostgreSQL cluster and configure the index by using the Inverted File
• DLaunch an Amazon DocumentDB cluster that has an IVFFlat index and a high probe value.

Explanation

Option B is the optimal solution because it maximizes similarity search accuracy and performance for a small, proprietary dataset while maintaining low operational complexity. Amazon MemoryDB is a fully managed, in-memory database that provides microsecond-level latency, making it ideal for real-time RAG workloads that require fast vector similarity searches. For small datasets with low index counts, the Hierarchical Navigable Small World (HNSW) algorithm is recommended by AWS for its high recall and accuracy. Unlike approximate methods optimized for massive datasets, HNSW excels at returning the most semantically relevant vectors with minimal loss of precision, which directly improves the quality of responses generated by the Amazon Bedrock foundation model. Vertical scaling in MemoryDB is sufficient for this use case because the dataset size is limited. Scaling up instance size provides increased memory and compute capacity without the complexity of managing distributed indexes or sharding strategies. This simplifies operations while maintaining predictable performance.

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