Option A is incorrect. Snowflake recommends executing queries that call Cortex AI SQL functions like EMBED_TEXT_768 with a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance. Snowpark-optimized warehouses are recommended for workloads with large memory requirements or specific CPU architectures, typically for ML training, not for general Cortex AI function calls. Option B is incorrect. The Snowpark Python library explicitly states that it does not support the VECTOR_COSINE_SIMILARITY function, meaning it does not 'fully support all vector similarity functions'. Option C is incorrect. The VECTOR data type is not supported as clustering keys. Option D is correct. After generating embeddings (e.g., storing them in a VECTOR column like issue vec ), vector similarity functions can be effectively used in SQL queries with ORDER BY and LIMIT clauses to retrieve the most similar results, as demonstrated with VECTOR_COSINE_SIMILARITY in a RAG example. This pattem applies to VECTOR_INNER_PRODUCT as well. Option E is incorrect. The VECTOR data type is not supported for use with dynamic tables. Additionally, Snowflake Cortex functions (including EMBED_TEXT_768 ) do not support dynamic tables.