You have a Microsoft SQL Server 2025 database that contains a table named products.

The correct answer is C because the current query uses VECTOR_DISTANCE( ' cosine ' , @query_vector, embedding) , which performs an exact vector distance calculation . Microsoft explicitly documents that VECTOR_DISTANCE does not use a vector index , even when one exists. Exact k-nearest-neighbor searches can require scanning large numbers of vectors and calculating distances row by row, which explains the observed CPU spikes and increased semantic-search latency during peak load.

A vector index on products.embedding is specifically designed to accelerate nearest-neighbor search, and VECTOR_SEARCH is the Transact-SQL function that can use that approximate nearest-neighbor index when the index metric matches the query metric. Using METRIC = ' cosine ' preserves the same similarity metric currently used by App1 while reducing the amount of computation required.

The other choices are incorrect:

A normalizes vectors but does not replace the expensive exact search with indexed ANN retrieval.

B replaces semantic vector search with lexical full-text search and changes the feature’s behavior.

D a standard nonclustered B-tree index cannot accelerate vector-distance operations.

E converting embeddings to VARBINARY(8000) removes native vector semantics and does not solve the performance problem.

Therefore, the exam-aligned solution is create a vector index and use VECTOR_SEARCH with cosine distance .