Q12Operational Efficiency and Optimization for GenAI Applications
A financial services company is building a Retrieval Augmented Generation (RAG) application to help investment analysts query complex financial relationships spanning multiple investment vehicles, market sectors, and regulatory environments. The dataset includes highly interconnected entities with multi-hop relationships. Analysts must be able to review these relationships holistically to deliver accurate investment guidance. The application must provide comprehensive answers that include indirect relationships among financial entities. The application must return responses in under 3 seconds. Which solution meets these requirements with the **LEAST** operational overhead?
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Discussion · 4
A 1
Feels like A is the intended answer here. The wording is tricky.
A 1
The core challenge is multi-hop relationship traversal across interconnected financial entities — exactly what graph databases are designed for. Neptune Analytics is purpose-built for fast graph queries, and Bedrock Knowledge Bases with Graph RAG handles the orchestration automatically, meaning the team writes no custom traversal logic. The "least operational overhead" constraint rules out anything requiring manual infrastructure management, custom logic layers, or bespoke indexing systems.
A 1
LEAST operational overhead.
A 1
Since graph-based inference is required, I think the answer is A.