Databricks Machine Learning Associate Question 372
Single answerYou are tasked with applying a custom transformation to a PySpark DataFrame column using a user-defined function (UDF). The transformation logic involves using pandas operations. Which of the following statements about implementing this functionality is correct?
- A
You can directly use pandas functions inside the UDF without any additional steps.
- B
You must ensure the input types to the UDF are converted to pandas-compatible data types before using pandas operations.
- C
Using pandas code inside a PySpark UDF is not supported, and you must only use PySpark functions.
- D
You should use pandas UDFs (also known as vectorized UDFs) instead of regular PySpark UDFs to efficiently apply pandas operations.
Show answer and explanation
Correct answer: D
Explanation
To use pandas operations within PySpark, the most efficient approach is to use pandas UDFs (vectorized UDFs). These UDFs allow you to run pandas code in parallel across the Spark cluster, harnessing both pandas' capabilities and PySpark's distributed computing power. While regular PySpark UDFs can theoretically use pandas code, they are not designed for efficient integration with pandas operations.
- A. Incorrect.
Incorrect. You cannot directly use pandas functions inside a PySpark UDF without additional steps, as PySpark operates on distributed data and pandas works on in-memory data.
- B. Incorrect.
Incorrect. While it is true that input data must be compatible with pandas, this option does not address the most efficient way to combine pandas operations with PySpark.
- C. Incorrect.
Incorrect. This is false; pandas code can be used inside UDFs, but it requires the correct approach, such as using pandas UDFs.
- D. Correct.
Correct. Pandas UDFs (also called vectorized UDFs) allow efficient operations with pandas code while leveraging the distributed nature of PySpark.