SnowPro Specialty: Gen AI Question 122
Single answerVECTOR_INNER_PRODUCTA retail company stores product-description embeddings in a Snowflake table as VECTOR columns. A data engineer needs to implement semantic search for a user query embedding and wants the results ranked from most similar to least similar using SQL only. The embedding model used for both the stored products and the query produces normalized vectors with unit length. Which SQL approach is the most appropriate when using VECTOR_INNER_PRODUCT?
- A
Select rows and order by VECTOR_INNER_PRODUCT(product_embedding, :query_embedding) DESC.
- B
Select rows and order by VECTOR_INNER_PRODUCT(product_embedding, :query_embedding) ASC, because smaller inner products indicate higher similarity.
- C
Use VECTOR_INNER_PRODUCT only after converting both vectors to VARIANT, because the function does not operate on VECTOR columns directly.
- D
Use VECTOR_INNER_PRODUCT and then apply ABS() before sorting DESC, because negative values should be treated as equally similar to positive values.
Show answer and explanation
Correct answer: A
Explanation
VECTOR_INNER_PRODUCT computes the dot product of two vectors. In practical semantic search workflows, if embeddings are normalized to unit length before storage and query-time comparison, the dot product becomes equivalent to cosine similarity. In that case, the correct ranking pattern is ORDER BY VECTOR_INNER_PRODUCT(stored_vector, query_vector) DESC. A common mistake is to sort ascending or to transform the score in ways that break the meaning of the similarity metric, such as applying ABS(). Snowflake vector functions are intended to operate on VECTOR columns directly, which supports SQL-based similarity retrieval patterns used in Gen AI and retrieval-augmented generation implementations.
- A. Correct.
Correct. When embeddings are normalized to unit length, the inner product is equivalent to cosine similarity, so larger values indicate greater semantic similarity. Ordering by VECTOR_INNER_PRODUCT(...) DESC is therefore the appropriate way to rank the most relevant products first in a semantic search scenario.
- B. Incorrect.
Incorrect. This reverses the ranking logic. For normalized vectors, a higher inner product means the vectors point in a more similar direction. Ordering ascending would put the least similar results first, which is the opposite of the desired behavior in semantic search.
- C. Incorrect.
Incorrect. VECTOR_INNER_PRODUCT is designed to work with Snowflake VECTOR data. Converting embeddings to VARIANT is unnecessary and would not be the correct pattern for vector similarity search. This distractor reflects a misconception that vector functions require semi-structured conversion before use.
- D. Incorrect.
Incorrect. Taking the absolute value would distort similarity semantics. A strongly negative inner product indicates vectors pointing in opposite directions, which is not equivalent to strong positive similarity. Using ABS() could incorrectly rank dissimilar items as highly relevant.