Consider the following Snowpark code snippet designed to process data from an event table and calculate a rolling average:

After deploying this code, you observe that the rolling average calculation is significantly slower than expected, even though the virtual warehouse is adequately sized. What is the MOST effective way to optimize the performance of this rolling average calculation in Snowpark, considering the size of window_size ?

• A. Use the SNOWFLAKML.FORECAST package in the Snowpark DataFrame to forecast the rolling average of events in each of the user session.
• B. Reduce the 'window_size' parameter. Smaller windows inherently require less computation.
• C. Materialize the intermediate 'event_df DataFrame into a temporary table using before applying the window function.
• D. Use the 'rangeBetween' method instead of 'rowsBetween' in the 'Windows specification, as it is generally more efficient for numerical data.
• E. Utilize Snowflake's APPROX AVG aggregate function in conjunction with a custom UDF to estimate the rolling average, trading off accuracy for performance.

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You have a Snowpark DataFrame 'employees df representing employee data'. You need to update the 'salary' column for employees in the 'Sales' department by applying a 10% increase. Which of the following Snowpark code snippets correctly performs this update? Assume a Snowflake table named 'employees' exists and 'employees df' is correctly created from it.

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

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You are developing a Snowpark application to load data into a Snowflake table named 'SALES DATA. The DataFrame 'sales_df contains new sales records. You need to insert these records into 'SALES DATA. Which of the following Snowpark DataFrame methods will efficiently perform this operation, considering potential data type mismatches between the DataFrame and the target table? Assume no explicit schema definition is necessary.

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

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You have a Snowflake table 'PRODUCT REVIEWS' with columns 'product_id', 'review_text', 'review_date', and 'sentiment_score'. You are using Snowpark to build a data pipeline that analyzes these reviews and identifies potentially malicious or spam reviews. Based on a machine learning model deployed as a Snowflake UDF, you have identified a set of 'product_id' values that are associated with malicious reviews, and are stored in a python list called 'malicious_product_ids'. You need to delete all rows from the 'PRODUCT REVIEWS' table where the 'product_id' is present in the list. Assume 'malicious_product_ids' contains thousands of product IDs. Which of the following approaches is the MOST efficient and scalable for deleting these records using Snowpark?

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

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Consider a Snowflake table 'sales_data' with a VARIANT column 'order_details' containing an array of JSON objects, where each object represents an item in an order. Each item object has fields like 'quantity', and 'price'. You need to calculate the total price for each order by summing the product of 'quantity' and 'price' for all items in the 'order_details' array. Which of the following Snowpark Python snippets correctly accomplishes this?

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

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