Your team is building an AI model serving application using Azure Cosmos DB for NoSQL as the vector database. The application stores embeddings for…

In Azure Cosmos DB for NoSQL, vector embeddings are large arrays. If not explicitly excluded from the standard indexing policy, Cosmos DB attempts to index every element, leading to excessive Request Unit (RU) consumption and storage costs. Vector indexes are defined separately, so the vector field must be excluded from standard indexing. Additionally, Strong consistency requires reading from a quorum, which doubles the RU cost for read operations compared to Eventual consistency. Switching to Eventual consistency reduces RU consumption and improves read latency.

A. Incorrect because Cosmos DB does not support composite indexes that combine scalar fields with vector embedding properties.

C. Incorrect because replicating data via change feed does not improve the query performance or reduce RU consumption of the primary database.

- Microsoft Learn: "Vector search in Azure Cosmos DB for NoSQL"

Section: Vector indexing policies. (https://learn.microsoft.com/en-us/azure/cosmos-db/nosql/vector-search)

- Microsoft Learn: "Consistency levels in Azure Cosmos DB"

Section: Consistency levels and throughput. (https://learn.microsoft.com/en-us/azure/cosmos-db/consistency-levels)