You are using Snowflake Cortex to analyze customer reviews. You have created a vector embedding for each review using a UDF that calls a remote LLM inference endpoint. Now you need to perform a similarity search to identify reviews that are similar to a given query review. Which of the following SQL queries leveraging vector functions in Snowflake is the MOST efficient and appropriate way to achieve this, assuming the 'REVIEW EMBEDDINGS' table has columns 'review_id' and 'embedding' (a VECTOR column) and query_embedding' is a pre-computed vector embedding?

• A. Option D
• B. Option A
• C. Option C
• D. Option E
• E. Option B

You are analyzing customer transaction data in Snowflake to identify fraudulent activities. The 'TRANSACTION AMOUNT' column exhibits a right-skewed distribution. Which of the following Snowflake queries is MOST effective in identifying outliers based on the Interquartile Range (IQR) method, specifically targeting unusually large transaction amounts? Assume IQR is already calculated as variable and QI as and Q3 as in snowflake session.

• A. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT + 3 FROM TRANSACTIONS);
• B. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT WITHIN GROUP (ORDER BY TRANSACTION_AMOUNT) FROM TRANSACTIONS);
• C. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT < qi - (1.5 iqr);
• D. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION AMOUNT > q3 + (1.5 iqr);
• E. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT MEDIAN(TRANSACTION AMOUNT) FROM TRANSACTIONS);

You are building a machine learning model using Snowpark Python to predict house prices. The dataset contains a feature column named 'location' which contains free-form text descriptions of house locations. You want to leverage a pre-trained Large Language Model (LLM) hosted externally to extract structured location features like city, state, and zip code from the free-form text within Snowpark. You want to minimize the data transferred out of Snowflake. Which approach is most efficient and secure?

• A. Use to load the 'location' column data into a Pandas DataFrame, call the external LLM API in your Python script to enrich the location data and then use to store the enriched data back into a Snowflake table.
• B. Use Snowpark's 'createOrReplaceStage' to create an external stage pointing to the LLM API endpoint. Load the 'location' data into this stage and call the LLM API directly from the Snowflake stage using SQL.
• C. Create a Snowpark User-Defined Function (UDF) that calls the external LLM API. Pass the 'location' column data to the UDF and retrieve the structured location features. Then apply the UDF directly on the Snowpark DataFrame.
• D. Use the Snowflake Connector for Python to directly query the 'location' column and call the external LLM API from the connector. Then write the updated data into a new table.
• E. Create a Snowflake External Function that calls the external LLM API. Pass the 'location' column data to the External Function and retrieve the structured location features. Then apply the External Function directly on the Snowpark DataFrame.

You are analyzing sales data in Snowflake using Snowpark to identify seasonality. You have a table named 'SALES DATA with columns 'SALE DATE (TIMESTAMP NTZ) and 'AMOUNT (NUMBER). You want to calculate the rolling average sales for each week over a period of 12 weeks using a Snowpark DataFrame. Which of the following Snowpark code snippets correctly implements this calculation?

• A.
  
• B.
  
• C.
  
• D.
  
• E.