SPS-C01 Exam Dumps | You have a Snowpark DataFrame named with the following schema: 'product

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Snowflake.SPS-C01.v2026-08-07.q151
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Question 70/151

You have a Snowpark DataFrame named with the following schema: 'product_id' (INTEGER), (STRING), 'category' (STRING), 'price' (FLOAT), and 'description' (STRING). You want to perform several data cleaning and transformation steps. Which of the following operations can be efficiently chained together using Snowpark DataFrames to clean null values in 'description', replace special characters in 'product_name' and standardize 'category' values? Select all that apply:

A. Using the method to replace null values in the 'description' column with a default string 'No description available'.

B. Using the function to remove special characters (e.g., '$', '#, '@') from the 'product_name' column using a regular expression.

C. Using a UDF (User-Defined Function) written in Python to standardize the 'category' column by converting all values to lowercase and removing leading/trailing spaces.

D. Using the 'coalesce' function to fill null values in 'description' with values from a separate 'backup_description' column (if available).

E. Manually iterating through each row of the DataFrame and applying Python string manipulation functions to clean the data. (e.g. row['description'] =

Correct Answer: A,B,D

Options A, B, and D can be efficiently chained using Snowpark DataFrame operations. Option A Cna.fill()') is a built-in method for handling null values. Option B is a SQL function available in Snowpark for string manipulation. Option D ('coalesce()') effectively fills null values from another column if present. Option C, using a UDF for string standardization, is viable but potentially less efficient than using built-in functions if possible. Option E is extremely inefficient as it forces data transfer to the client and row-by-row processing instead of leveraging Snowflake's parallel processing capabilities. Chaining operations allows Snowpark to optimize the execution plan and potentially perform these transformations in a single pass over the data. UDF execution might introduce overhead.

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