AIP-C01 · Question #1
A financial services company is building a customer support application that retrieves relevant financial regulation documents from a database based on semantic similarity to user queries. The…
The correct answer is A. Use Amazon OpenSearch Serverless to provide vector search capabilities and metadata filtering. Option A is the optimal solution because it provides scalable semantic search, rich metadata filtering, and tight integration with Amazon Bedrock while minimizing operational overhead. Amazon OpenSearch Serverless is designed for high-volume, low-latency search workloads and…
Question
A financial services company is building a customer support application that retrieves relevant financial regulation documents from a database based on semantic similarity to user queries. The application must integrate with Amazon Bedrock to generate responses. The application must search documents in English, Spanish, and Portuguese. The application must filter documents by metadata such as publication date, regulatory agency, and document type. The database stores approximately 10 million document embeddings. To minimize operational overhead, the company wants a solution that minimizes management and maintenance effort while providing low-latency responses for real-time customer interactions. Which solution will meet these requirements?
Options
- AUse Amazon OpenSearch Serverless to provide vector search capabilities and metadata filtering.
- BDeploy an Amazon Aurora PostgreSQL database with the pgvector extension. Store embeddings
- CUse Amazon S3 Vectors to configure a vector index and non-filterable metadata fields. Integrate
- DSet up an Amazon Neptune Analytics database with a vector index. Use graph-based retrieval
How the community answered
(19 responses)- A74% (14)
- B5% (1)
- C5% (1)
- D16% (3)
Explanation
Option A is the optimal solution because it provides scalable semantic search, rich metadata filtering, and tight integration with Amazon Bedrock while minimizing operational overhead. Amazon OpenSearch Serverless is designed for high-volume, low-latency search workloads and removes the need to manage clusters, capacity planning, or scaling policies. With support for vector search and structured metadata filtering, OpenSearch Serverless enables efficient similarity search across 10 million embeddings while applying constraints such as language, publication date, regulatory agency, and document type. This is critical for financial services use cases where relevance and compliance depend on precise filtering. Integrating OpenSearch Serverless with Amazon Bedrock Knowledge Bases enables a fully managed RAG workflow. The knowledge base handles embedding generation, retrieval, and context assembly, while Amazon Bedrock generates responses using a foundation model. This significantly reduces custom glue code and operational complexity. Multilingual support is handled at the embedding and retrieval layer, allowing documents in English, Spanish, and Portuguese to be searched semantically without language-specific query logic. OpenSearch's distributed architecture ensures consistent low-latency responses for real- time customer interactions.
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