SnowPro Specialty: Gen AI Question 109
Single answerEMBED_TEXT_1024A retail company stores product descriptions in a Snowflake table and wants to build semantic search so analysts can find similar products using natural-language queries. The team decides to use Snowflake Cortex EMBED_TEXT_1024 to generate embeddings directly in SQL. They want the design to minimize similarity-calculation errors and keep query embeddings comparable to the stored product-description embeddings. Which approach should they use?
- A
Generate embeddings for both the stored product descriptions and the user search queries using the same EMBED_TEXT_1024 model, then compare vectors using a similarity metric such as cosine similarity.
- B
Generate embeddings for product descriptions with EMBED_TEXT_1024, but use a different embedding model for user search queries so the search captures broader semantics.
- C
Store only the raw product-description text and compute semantic similarity by comparing the lengths of the strings returned by EMBED_TEXT_1024.
- D
Use EMBED_TEXT_1024 on product descriptions, then average the resulting vectors into a single table-level embedding and compare every user query only to that aggregate vector.
Show answer and explanation
Correct answer: A
Explanation
EMBED_TEXT_1024 is used to convert text into 1024-dimensional vector embeddings for downstream tasks such as semantic search, clustering, and similarity-based retrieval. In a realistic Snowflake implementation, teams typically precompute embeddings for each row of source text and store them, then generate an embedding for the incoming user query with the same model and compare vectors using a similarity function such as cosine similarity. The key best practice is model consistency: both the indexed content and the query text should be embedded with the same model so the vectors remain comparable in the same embedding space. Mixing models or reducing all row embeddings into a single aggregate vector undermines retrieval quality. This aligns with Snowflake Cortex guidance for embedding-based semantic workflows using EMBED_TEXT_1024.
- A. Correct.
Correct. For semantic search, embeddings being compared should be produced by the same embedding model so they exist in the same vector space. A common pattern is to precompute and store embeddings for source text, then embed the incoming query with the same model and compute similarity, often with cosine similarity. This minimizes inconsistencies and supports reliable nearest-neighbor style retrieval.
- B. Incorrect.
Incorrect. A common misconception is that mixing embedding models improves semantic coverage. In practice, embeddings from different models are generally not directly comparable because they may have different vector spaces, distributions, and dimensions. Using different models for stored documents and queries can degrade or invalidate similarity results.
- C. Incorrect.
Incorrect. EMBED_TEXT_1024 produces vector embeddings, not a semantic score based on string length. Comparing string lengths does not represent meaning and would not support semantic retrieval. The value of EMBED_TEXT_1024 is in transforming text into numeric vectors that encode semantic relationships.
- D. Incorrect.
Incorrect. Averaging all product-description embeddings into one aggregate vector removes item-level granularity. Semantic search requires comparing a query embedding against individual document or product embeddings to identify the closest matches. A single table-level average cannot return relevant per-product results.