SPS-C01 Exam Dumps | You are tasked with processing a Snowpark DataFrame named 'orders df that contains order information.

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Question 64/151

You are tasked with processing a Snowpark DataFrame named 'orders df that contains order information. The DataFrame includes the following columns: 'order _ id' (INTEGER), 'customer_id' (INTEGER), 'order_date' (DATE), 'order_total' (STRING), and 'discount_code' (STRING). The 'order_total' column contains values with leading dollar signs and commas (e.g., '$1 ,234.56'). The column can contain codes like 'SAVEIO', 'SAVE20', or be NULL. Your goal is to create a new DataFrame 'transformed_df that includes the following transformations: 1 . Convert the 'order_total' column to a numeric value (DOUBLE) after removing the dollar signs and commas. 2. Apply a discount based on the 'discount_code'. If the 'discount_code' is 'SAVEIO', apply a 10% discount; if it's 'SAVE20', apply a 20% discount. If the 'discount_code' is NULL or any other value, apply no discount (0%). 3. Calculate the 'final_total' after applying the discount. Which of the following code snippets correctly and efficiently implements these transformations using Snowpark?

Correct Answer: A

Option A correctly implements all transformations efficiently using Snowpark functions. It converts 'order_totar to a numeric value, applies the discount based on the using 'when' , and calculates the 'final_totar. It avoids using IJDFs or 'collect' operations, which can be less efficient. Using 'lit' with numeric values isn't necessary or best practice, so option B is less preferable. Option C attempts to use a IJDF, which is less efficient than using built-in Snowpark functions. Also 'to_number' and for IJDF is not required. Option D calculates the discount amount directly instead of the discount rate. Option E attempts to use 'rdd.map' which is not available and it's generally advised against as it removes parallelism.

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