AIP-C01 · Question #11
A financial services company is developing a Retrieval Augmented Generation (RAG) application to help investment analysts query complex financial relationships across multiple investment vehicles…
The correct answer is A. Use Amazon Bedrock Knowledge Bases with GraphRAG and Amazon Neptune Analytics to store. Option A best satisfies the requirement to capture multi-hop, highly interconnected relationships with minimal operational overhead. Traditional vector similarity search excels at finding semantically similar text but is not optimized for reasoning over explicit…
Question
A financial services company is developing a Retrieval Augmented Generation (RAG) application to help investment analysts query complex financial relationships across multiple investment vehicles, market sectors, and regulatory environments. The dataset contains highly interconnected entities that have multi-hop relationships. Analysts must examine relationships holistically to provide accurate investment guidance. The application must deliver comprehensive answers that capture indirect relationships between financial entities and must respond in less than 3 seconds. Which solution will meet these requirements with the LEAST operational overhead?
Options
- AUse Amazon Bedrock Knowledge Bases with GraphRAG and Amazon Neptune Analytics to store
- BUse Amazon Bedrock Knowledge Bases and an Amazon OpenSearch Service vector store to
- CUse Amazon OpenSearch Serverless vector search with k-nearest neighbor (k-NN). Implement
- DUse Amazon DynamoDB to store financial data in a custom indexing system. Use AWS Lambda
How the community answered
(46 responses)- A76% (35)
- B13% (6)
- C4% (2)
- D7% (3)
Explanation
Option A best satisfies the requirement to capture multi-hop, highly interconnected relationships with minimal operational overhead. Traditional vector similarity search excels at finding semantically similar text but is not optimized for reasoning over explicit entity-to-entity relationships, especially when analysts need indirect, multi-hop connections. Graph-based retrieval is designed specifically for these kinds of relationship traversals. GraphRAG combines retrieval-augmented generation with graph-aware context selection. By representing entities and their relationships in a graph store, the system can traverse multiple hops to assemble a holistic set of relevant facts. This improves completeness and reduces the chance that the model misses indirect relationships that are essential for accurate investment Amazon Neptune Analytics provides a managed graph analytics environment capable of efficiently traversing and analyzing complex relationship networks. When integrated with Amazon Bedrock Knowledge Bases, it reduces custom engineering by providing managed ingestion, retrieval, and orchestration patterns suitable for GenAI applications. This lowers operational overhead compared to building and maintaining custom multi-stage retrieval logic. Meeting the sub-3-second requirement is also more feasible with a graph-optimized engine because multi-hop traversals can be executed efficiently compared to chaining multiple vector searches and joining results in an application layer. The managed nature of Knowledge Bases and Neptune Analytics reduces maintenance, scaling, and operational burden while enabling strong performance.
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