AIP-C01 practice question 8 of 15

A financial services company is developing a Retrieval Augmented Generation (RAG) application to help investment analysts query complex financial…

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?

Answer options

A: Use Amazon Bedrock Knowledge Bases with GraphRAG and Amazon Neptune Analytics to store financial data. Analyze multi-hop relationships between entities and automatically identify related information across documents. Correct answer

B: Use Amazon Bedrock Knowledge Bases and an Amazon OpenSearch Service vector store to implement custom relationship identification logic that uses AWS Lambda to query multiple vector embeddings in sequence.

C: Use Amazon OpenSearch Serverless vector search with k-nearest neighbor (k-NN). Implement manual relationship mapping in an application layer that runs on Amazon EC2 Auto Scaling.

D: Use Amazon DynamoDB to store financial data in a custom indexing system. Use AWS Lambda to query relevant records. Use Amazon SageMaker to generate responses.

Correct Answer:

A. Use Amazon Bedrock Knowledge Bases with GraphRAG and Amazon Neptune Analytics to store financial data. Analyze multi-hop relationships between entities and automatically identify related information across documents.

Explanation

The problem requires querying highly interconnected data with multi-hop relationships, which is the primary strength of a graph database. Amazon Neptune Analytics is a graph analytics database engine optimized for this purpose. GraphRAG is a technique specifically designed to traverse these relationships to find relevant context for a RAG application. Using this with Amazon Bedrock Knowledge Bases provides a managed, low-overhead solution that directly addresses the need to analyze complex, indirect relationships comprehensively and quickly, meeting the sub-3-second response requirement.

Why Incorrect

B. Vector stores are not inherently optimized for multi-hop relationship traversal; implementing this with sequential Lambda queries adds complexity, latency, and operational overhead.
C. This option requires manual relationship mapping and self-managed EC2 instances, which creates significant operational overhead, contradicting a key requirement.
D. DynamoDB is not a graph database and is ill-suited for efficiently querying complex, multi-hop relationships without significant custom logic and performance trade-offs.

References

1. AWS News Blog (2024)

"New Amazon Bedrock Knowledge Bases with GraphRAG and Amazon Neptune Analytics for enhanced RAG". This official announcement describes the solution for "uncovering multi-hop relationships across various data sources to deliver more comprehensive and well-rounded responses."

2. AWS Documentation

Amazon Neptune

"What Is Amazon Neptune?". The documentation states

"Neptune is a purpose-built

high-performance graph database engine... It is optimized for storing billions of relationships and querying the graph with milliseconds latency."

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