Databricks Machine Learning Associate Question 365
Select 3You are working on a large dataset in Databricks and need to apply a trained machine learning model to generate predictions for each row. To optimize performance, you decide to use a Pandas UDF. Which of the following steps are necessary to correctly implement and apply a Pandas UDF for this purpose?
- A
Define the UDF using the
@pandas_udfdecorator with the appropriate return type. - B
Prepare the input DataFrame by partitioning it explicitly before applying the Pandas UDF.
- C
Ensure the model is loaded or initialized inside the Pandas UDF to avoid serialization issues.
- D
Use the
applymethod on the DataFrame to apply the Pandas UDF. - E
Return a Pandas Series or DataFrame from the Pandas UDF to match the specified return type.
Show answer and explanation
Correct answers: A, C, E
Explanation
To apply a model in parallel using a Pandas UDF in Databricks, you need to define the UDF with the @pandas_udf decorator and specify its return type. The model should be initialized or loaded within the UDF to ensure it is accessible on the worker nodes. The Pandas UDF must return a Pandas Series or DataFrame that matches the return type declared in the UDF definition. Spark automatically manages partitioning, and the Pandas UDF should be applied using Spark-specific methods such as withColumn or select, not the Pandas apply method.
- A. Correct.
Correct. When defining a Pandas UDF, you must use the
@pandas_udfdecorator and specify the return type (e.g., FloatType, IntegerType). This ensures the function can be applied across partitions in parallel. - B. Incorrect.
Incorrect. Explicit partitioning of the DataFrame is not required when using a Pandas UDF. Databricks automatically handles partitioning for distributed processing.
- C. Correct.
Correct. Loading or initializing the model inside the Pandas UDF is critical to avoid serialization issues when the UDF is distributed across worker nodes.
- D. Incorrect.
Incorrect. The
applymethod is used for local Pandas DataFrames, not for distributed Spark DataFrames. Instead, you should use thewithColumnmethod orselectwhen applying a Pandas UDF in PySpark. - E. Correct.
Correct. Pandas UDFs must return a Pandas Series or DataFrame that matches the specified return type. This ensures compatibility with the Spark DataFrame structure.