Databricks Machine Learning Associate Question 363
Select 3You are working on a machine learning pipeline in Databricks where you need to apply a pre-trained model to a large dataset stored as a Spark DataFrame. The model is implemented in Python and expects a Pandas DataFrame as input and returns a Pandas DataFrame as output. To apply this model efficiently in parallel across the dataset, which of the following steps should you take?
- A
Define a Pandas UDF using the PySpark
pandas_udfdecorator with the appropriate return type. - B
Use the Pandas UDF to process the data by splitting the Spark DataFrame into batches of Pandas DataFrames.
- C
Apply the Pandas UDF to the Spark DataFrame using the
withColumnmethod to create a new column with the model predictions. - D
Convert the entire Spark DataFrame into a Pandas DataFrame and apply the model directly to it.
- E
Use the
mapPartitionsAPI instead of a Pandas UDF for better performance when applying the model.
Show answer and explanation
Correct answers: A, B, C
Explanation
To efficiently apply a Python-based pre-trained model to a large Spark DataFrame, you should use a Pandas UDF. The Pandas UDF processes the data in batches by converting Spark DataFrame partitions into Pandas DataFrames, which the model can handle. The pandas_udf decorator enables this functionality, and the withColumn method is used to apply the UDF and store the resulting predictions. Avoid converting the entire Spark DataFrame to a Pandas DataFrame, as this is not scalable, and using mapPartitions is less efficient for this specific use case.
- A. Correct.
Correct: The
pandas_udfdecorator is required to define a Pandas UDF, which allows PySpark to handle the conversion between Spark DataFrames and Pandas DataFrames in batches. - B. Correct.
Correct: A Pandas UDF processes data in batches, so the data is split into Pandas DataFrames that can be passed to the model for parallel processing.
- C. Correct.
Correct: The
withColumnmethod is commonly used to apply a Pandas UDF to a Spark DataFrame, enabling the creation of a new column with the model's predictions. - D. Incorrect.
Incorrect: Converting the entire Spark DataFrame into a Pandas DataFrame would require loading the entire dataset into memory, which is inefficient and not scalable for large datasets.
- E. Incorrect.
Incorrect: While
mapPartitionscan be used for custom parallel processing, it is not optimized for Pandas-based model application and lacks the ease of use provided by Pandas UDFs.