Databricks Machine Learning Associate Question 366
Select 2You are working with a large dataset in a Databricks notebook and have trained a machine learning model to predict house prices. You want to apply this model in parallel to a Spark DataFrame using a Pandas UDF. Which of the following steps are required to correctly implement this?
- A
Define the Pandas UDF with the
pandas_udfdecorator and specify the return type. - B
Ensure that the input DataFrame is converted into a Pandas DataFrame before applying the Pandas UDF.
- C
Pass the Spark DataFrame to the Pandas UDF directly using the
.apply()method. - D
Ensure that the Pandas UDF processes batches of data rather than row-by-row.
- E
Register the Pandas UDF as a SQL function before applying it to a Spark DataFrame.
Show answer and explanation
Correct answers: A, D
Explanation
To apply a model in parallel using a Pandas UDF, you need to define the UDF using the pandas_udf decorator and specify the return type. Spark automatically handles the conversion of data into Pandas batches for processing, so no manual conversion is required. Pandas UDFs process data in batches rather than row-by-row, making them efficient for parallel operations. While registering the UDF as a SQL function is possible, it is not necessary for applying it directly to a Spark DataFrame.
- A. Correct.
Correct. To use a Pandas UDF in Spark, you must define it using the
pandas_udfdecorator and specify the return type. This step is essential for Spark to understand how to process the data in parallel. - B. Incorrect.
Incorrect. While Pandas UDF processes data in Pandas DataFrames internally, you do not need to manually convert the input Spark DataFrame to a Pandas DataFrame. Spark handles this conversion automatically for batch processing.
- C. Incorrect.
Incorrect. Pandas UDFs are not applied to Spark DataFrames using the
.apply()method. Instead, they are applied as part of Spark SQL or DataFrame operations like.withColumn()or.select(). - D. Correct.
Correct. Pandas UDFs process data in batches, which allows them to operate efficiently on large datasets by parallelizing the workload.
- E. Incorrect.
Incorrect. While you can register a Pandas UDF as a SQL function, it is not a mandatory step for applying it to a Spark DataFrame. You can use Pandas UDFs directly in DataFrame operations without registering them as SQL functions.