SPS-C01 Exam Dumps | You have a Snowpark DataFrame named 'orders df with columns 'order id', 'customer id', 'order date',

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Question 77/151

You have a Snowpark DataFrame named 'orders df with columns 'order id', 'customer id', 'order date', and 'total amount'. You need to create a new DataFrame that contains only the 'customer_id" and the total number of orders placed by each customer. However, you want to perform this aggregation in parallel using a user-defined function (UDF) to improve performance. Which approach is MOST efficient and CORRECT?

A. Create a UDF that takes a customer ID as input and returns the count of orders for that customer, then apply this UDF to each distinct customer ID using 'map'.

B. Create a UDF that performs the entire aggregation and call it with 'orders_df. The UDF uses a Pandas DataFrame internally to perform the count.

C. Use 'groupBy' to group by 'customer_id' and then use the 'count' aggregate function. Do not use a UDF.

D. Create a UDF that takes an iterator of order IDs and returns the count. Apply this UDF to each partition of the DataFrame using 'maplnPartitions' .

E. Create a UDF that calculates the mode of total_amount, then apply the UDF to the DataFrame using 'select'.

Correct Answer: C

The most efficient and correct approach is to use the built-in 'groupBy' and 'count' functions. These functions are optimized for Snowflake's architecture and will generally outperform UDF-based solutions for simple aggregations. Using UDFs for simple tasks introduces overhead and can negate any potential performance benefits. While options A, B and D could achieve the result, they are less efficient. Option E is irrelevant to the problem.

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