AWS Certified Cloud Practitioner
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A financial services firm wants to store and query millions of embeddings from customer documents to power an AI assistant built on Amazon Bedrock. They need full control over database configuration. Which AWS service should they use to self-manage the embedding vector database?
Real Exam
Community
RRitesh
0
A
Amazon RDS
B
Amazon DynamoDB
C
Amazon OpenSearch Service
D
Amazon Aurora
E
Amazon Neptune
F
Amazon MemoryDB for Redis
Explanation:
Explanation
Amazon OpenSearch Service is the correct choice for this scenario because:
- Vector Database Capabilities: OpenSearch supports vector similarity search, which is essential for storing and querying embeddings efficiently
- Full Control: As a self-managed service, OpenSearch allows complete control over database configuration, clustering, and performance tuning
- Scalability: Can handle millions of embeddings with distributed architecture
- Integration with Amazon Bedrock: Works well with AI/ML services for embedding-based applications
- Financial Services Compliance: Supports security features needed for financial data
Other options are less suitable:
- Amazon RDS: Traditional relational database, not optimized for vector operations
- Amazon DynamoDB: NoSQL database, lacks native vector search capabilities
- Amazon Aurora: Relational database, not designed for vector embeddings
- Amazon Neptune: Graph database, not optimized for vector similarity search
- Amazon MemoryDB for Redis: In-memory database, can support vectors but OpenSearch is more purpose-built for this use case
OpenSearch provides the best combination of vector search capabilities, scalability, and configuration control for embedding storage and retrieval.
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