AWS Certified AI Practitioner AIF-C01 Practice Question
A team is adding semantic search to its application. They need a fully managed, MongoDB-compatible database that can store text embeddings in a vector index and perform similarity (k-nearest-neighbor) queries without managing any servers. Which AWS service meets these requirements?
Amazon DocumentDB (with MongoDB compatibility) now includes native vector search capabilities, allowing developers to store embeddings alongside other document data and run k-nearest-neighbor queries. Amazon Aurora MySQL-Compatible Edition, Amazon Neptune, and Amazon DynamoDB do not provide MongoDB compatibility, and none of them currently offer built-in vector indexing that satisfies the stated need.
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What are text embeddings and how are they used in vector search?
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Why is MongoDB compatibility important for this use case?
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What makes Amazon DocumentDB suitable for managing vector search compared to other AWS services like DynamoDB or Aurora?
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What are text embeddings and how are they used in applications?
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What is k-nearest-neighbor (k-NN) and how does it work in similarity searches?
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What are the benefits of using a fully managed database like Amazon DocumentDB for semantic search?
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AWS Certified AI Practitioner AIF-C01
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