SnowPro Specialty: Gen AI Question 127
Single answerVECTOR_COSINE_SIMILARITYA retail company stores product-description embeddings in a Snowflake table to power semantic search. Each row contains a VECTOR column named PRODUCT_EMBEDDING, generated with the same embedding model and dimensionality. At query time, the application computes a query embedding and needs to return the top 10 most semantically similar products. The developer notices that a SQL query using VECTOR_COSINE_SIMILARITY sometimes returns negative values and is unsure how to rank results correctly. Which approach should the developer use?
- A
Order results by VECTOR_COSINE_SIMILARITY(PRODUCT_EMBEDDING, :query_embedding) DESC and take the top 10 rows.
- B
Order results by VECTOR_COSINE_SIMILARITY(PRODUCT_EMBEDDING, :query_embedding) ASC because smaller cosine similarity means closer vectors.
- C
Filter out any rows where VECTOR_COSINE_SIMILARITY is less than 0 because negative values indicate an invalid embedding comparison.
- D
Normalize the cosine similarity values to a 0-1 range in SQL before sorting, because Snowflake only supports ranking positive similarity scores.
Show answer and explanation
Correct answer: A
Explanation
For semantic search in Snowflake, VECTOR_COSINE_SIMILARITY is used to compare two vectors of the same type and dimensionality and returns a cosine similarity score. The practical retrieval pattern is to compute the similarity between each stored embedding and the query embedding, then sort by that score in descending order to get the nearest semantic matches. A common misconception is to treat cosine similarity like a distance metric or to assume negative values are errors. In reality, negative values are valid mathematical results. Snowflake documentation for vector similarity functions and vector embeddings supports using VECTOR_COSINE_SIMILARITY directly for similarity ranking when embeddings are compatible.
- A. Correct.
Correct. VECTOR_COSINE_SIMILARITY returns a cosine similarity score where larger values indicate greater similarity. In semantic search, the standard pattern is to compute similarity between the stored embedding and the query embedding, sort in descending order, and return the top matches. Negative scores are valid and simply indicate dissimilar or oppositely oriented vectors in the embedding space.
- B. Incorrect.
Incorrect. This reverses the ranking logic. Cosine similarity is higher for more similar vectors, so sorting ascending would prioritize the least similar results. This confusion often comes from mixing up cosine similarity with cosine distance, which has opposite ranking behavior.
- C. Incorrect.
Incorrect. Negative cosine similarity values do not mean the embeddings are invalid. They are mathematically valid outputs of cosine similarity and can occur when vectors point in substantially different directions. Filtering them out may remove poor matches, but it is not required for correctness and should not be used because the values are assumed invalid.
- D. Incorrect.
Incorrect. Snowflake does not require cosine similarity scores to be transformed into a 0-1 range before sorting. VECTOR_COSINE_SIMILARITY can be used directly for ranking. Rescaling may be useful for presentation or downstream business logic, but it is not necessary for semantic search ordering.