AWS Certified AI 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
JJin
A
Amazon OpenSearch Service (self-managed cluster)
B
Knowledge Bases for Amazon Bedrock
C
Amazon DynamoDB
D
Amazon Redshift
Explanation:
Explanation:
Amazon OpenSearch Service with a self-managed cluster is the correct choice because:
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Full Control Over Database Configuration: The question specifically states the firm needs "full control over database configuration." Amazon OpenSearch Service allows customers to self-manage clusters where they have complete control over configuration, scaling, and management.
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Vector Database Capabilities: Amazon OpenSearch Service supports vector search capabilities through its k-nearest neighbors (k-NN) plugin, making it suitable for storing and querying embeddings.
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Comparison with Other Options:
- Knowledge Bases for Amazon Bedrock: This is a managed service that abstracts away the database management, so it doesn't provide "full control over database configuration."
- Amazon DynamoDB: While DynamoDB can store data, it's not specifically designed as a vector database for efficient similarity search of embeddings.
- Amazon Redshift: This is a data warehousing service optimized for analytical queries, not for vector similarity searches.
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Integration with Amazon Bedrock: Amazon OpenSearch Service integrates well with Amazon Bedrock for AI applications, allowing efficient storage and retrieval of embeddings for AI assistants.
This solution gives the financial services firm the control they need while providing the vector search capabilities required for their AI assistant.
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