Exam Databricks-Machine-Learning-Associate Topic 2 Question 29 Discussion

Actual exam question for Databricks's Databricks-Machine-Learning-Associate exam
Question #: 29
Topic #: 2
A machine learning engineer wants to parallelize the training of group-specific models using the Pandas Function API. They have developed the train_model function, and they want to apply it to each group of DataFrame df.
They have written the following incomplete code block:

Which of the following pieces of code can be used to fill in the above blank to complete the task?

Suggested Answer: B Vote an answer

The function mapInPandas in the PySpark DataFrame API allows for applying a function to each partition of the DataFrame. When working with grouped data, groupby followed by applyInPandas is the correct approach to apply a function to each group as a separate Pandas DataFrame. However, if the function should apply across each partition of the grouped data rather than on each individual group, mapInPandas would be utilized. Since the code snippet indicates the use of groupby, the intent seems to be to apply train_model on each group specifically, which aligns with applyInPandas. Thus, applyInPandas is a better fit to ensure that each group generated by groupby is processed through the train_model function, preserving the partitioning and grouping integrity.
Reference
PySpark Documentation on applying functions to grouped data: https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.GroupedData.applyInPandas.html

by andresmontero at Jul 21, 2025, 11:08 AM

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andresmontero
2025-07-21 11:09:17
Selected Answer: A
Use applyInPandas when you want to apply a Pandas function to each group of your data after groupBy. mapInPandas is more suitable for row-wise transformations.
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