Databricks Machine Learning Associate Question 370
Single answerYou are working on a Databricks notebook where you need to process a column of a Spark DataFrame using a custom transformation implemented in pandas. You decide to use a User-Defined Function (UDF) for this task. Which of the following statements about using pandas within a UDF is correct?
- A
You can directly use pandas functions inside a standard Python UDF without any additional considerations.
- B
To use pandas within a UDF, you must ensure that the UDF is defined as a pandas UDF (also known as a vectorized UDF).
- C
You cannot use pandas code inside a UDF as Spark DataFrames are incompatible with pandas.
- D
Using pandas inside a UDF is only allowed when the spark.sql.execution.arrow.pyspark.enabled configuration is set to true.
Show answer and explanation
Correct answer: B
Explanation
The correct answer is that pandas code can be used inside a UDF when the UDF is defined as a pandas UDF (also known as a vectorized UDF). This is because pandas UDFs are designed to allow efficient batch processing of Spark data using pandas, overcoming the inefficiencies of row-by-row processing in standard Python UDFs. While enabling Arrow can improve performance, it is not mandatory for pandas UDFs.
- A. Incorrect.
This is incorrect because standard Python UDFs operate on one row at a time, and using pandas directly without optimization would lead to inefficiencies and is not the recommended approach.
- B. Correct.
This is correct because pandas UDFs (vectorized UDFs) are explicitly designed to leverage pandas functionality, allowing batch processing of data for better performance.
- C. Incorrect.
This is incorrect because pandas code can be used in UDFs, specifically within pandas UDFs, which are compatible with Spark DataFrames.
- D. Incorrect.
This is incorrect because while enabling Arrow can improve the performance of pandas-to-Spark and Spark-to-pandas operations, it is not a strict requirement for using pandas inside a UDF.