GES-C01 Exam Dumps | A machine learning engineer is building a product recommendation system in Snowflake that uses item embeddings

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Question 140

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Question 140/171

A machine learning engineer is building a product recommendation system in Snowflake that uses item embeddings and customer query embeddings for similarity matching. They plan to use the VECTOR_L1_DISTANCE function to find the closest products to a user's query. Which statement accurately describes the cost and data type considerations for this approach?

A. The

B. Storing the item and query embeddings in

C. Executing the

D. Both

E. The maximum dimension for a

Correct Answer: D

Option A is incorrect because vector similarity functions, including

, do not incur token-based costs. Option B is incorrect. The VECTOR data type supports elements of either 32-bit integers ( INT ) or 32-bit floating-point numbers ( FLOAT ), and its dimension can be a positive integer up to 4096, regardless of the specific dimension value. Option C is incorrect.

does not incur token-based costs. Conversely, generating embeddings using functions like EMBED_TEXT_768 does incur compute costs based on input tokens. Option D is correct. Vector similarity functions, such as VECTOR_INNER_PRODUCT and VECTOR_L1_DISTANCE , are explicitly stated to not incur token-based costs, unlike embedding functions. Option E is incorrect. The maximum dimension for the VECTOR data type is 4096.

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