SnowPro Specialty: Gen AI Question 110
Single answerEMBED_TEXT_1024A retail company stores product descriptions in a Snowflake table and wants to power semantic search for a support application. The team decides to generate vector embeddings directly in Snowflake using EMBED_TEXT_1024. They need a design that is reliable for production and minimizes downstream errors when computing similarity. Which approach is the BEST choice?
- A
Generate embeddings for both the stored product descriptions and incoming user queries with the same EMBED_TEXT_1024 model, persist the vectors, and compare them using vector similarity functions.
- B
Generate embeddings for product descriptions with EMBED_TEXT_1024, but encode user queries with a different embedding model because query text is shorter than document text.
- C
Store the raw product description text only, and at query time use EMBED_TEXT_1024 on the query while comparing the query embedding directly to the VARCHAR product descriptions.
- D
Use EMBED_TEXT_1024 to create 1024-character summaries of each product description, then run similarity comparisons on those summaries.
Show answer and explanation
Correct answer: A
Explanation
EMBED_TEXT_1024 is used to convert text into a fixed-size vector representation suitable for semantic similarity and retrieval workflows. In a production semantic search design, document text is typically embedded once and stored, while incoming queries are embedded at runtime using the same model. Similarity is then computed between vectors, not between vectors and raw text. Using different embedding models for documents and queries is a common error because embeddings from different models are not generally comparable. Another frequent misconception is interpreting '1024' as output text length, but it refers to vector dimensionality. Snowflake documentation and Cortex AI best practices emphasize using embeddings for retrieval, storing vectors for reuse, and applying vector similarity functions consistently within the same embedding space.
- A. Correct.
Correct. For semantic search, the standard production pattern is to embed both the corpus text and the incoming query using the same embedding model so the vectors are in the same embedding space. Persisting document embeddings avoids repeatedly recomputing them, and Snowflake supports vector workflows for storing embeddings and performing similarity calculations. This approach is the most reliable and operationally efficient.
- B. Incorrect.
Incorrect. A common misconception is that shorter queries should use a different embedding model. In semantic retrieval, mixing embedding models for indexed documents and queries can place vectors in incompatible spaces, leading to poor or invalid similarity results. Best practice is to use the same embedding model for both sides of retrieval unless the system is specifically designed for model alignment.
- C. Incorrect.
Incorrect. Vector similarity requires vectors on both sides of the comparison. A query embedding cannot be meaningfully compared directly to raw VARCHAR text. The product descriptions must also be embedded first and stored as vectors, either ahead of time or generated in a preprocessing step.
- D. Incorrect.
Incorrect. EMBED_TEXT_1024 generates numerical vector embeddings, not text summaries and not 1024-character outputs. The '1024' refers to the embedding dimensionality, not a text length limit or summarization behavior. This option reflects a misunderstanding of what embedding functions produce.