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You have an Azure SQL database That contains a table named dbo.Products, dbo.Products contains three columns named Embedding Category, and Price. The Embedding column is defined as VECTOR(1536). You use Ai_GENERME_EMBEDOINGS and VECTOR_SEARCH to support semantic search and apply additional filters on two columns named Category and Price. You plan to change the embedding model from text-embedding-ada-002 to text-embedding-3-smalL Existing rows already contain embeddings in the Embedding column. You need to implement the model change. Applications must be able to use VECTOR_SEARCH without runtime errors. What should you do first?
Correct Answer: A
When you change embedding models, the stored vectors should be treated as belonging to a different embedding space unless you intentionally keep the entire corpus consistent. Microsoft's vector guidance notes that when most or all embeddings are replaced with fresh embeddings from a new model, the recommended practice is to reload the new embeddings and, for large-scale replacement scenarios, consider dropping and recreating the vector index afterward so search quality remains predictable. This question also says applications must continue to use VECTOR_SEARCH without runtime errors . VECTOR_SEARCH requires compatible vector dimensions, and the vector column already exists. Azure OpenAI documentation shows that text-embedding-ada-002 is fixed at 1536 dimensions and text- embedding-3-small supports up to 1536 dimensions . That means the migration can remain compatible with a VECTOR(1536) column, but the right implementation step is still to re-embed the existing rows so the table does not contain a mixed corpus produced by different models. The other options are not appropriate: * B normalization does not solve a model migration problem. * C converting the vector column to nvarchar(max) would break vector-native search design. * D a vector index improves performance, but it does not migrate old embeddings to the new model.
