Databricks Machine Learning Associate Question 376
Select 3You are tasked with building separate machine learning models for different customer segments in your dataset, and you intend to use the Pandas Function API in Databricks to achieve this. Each customer segment is identified by a unique 'segment_id' column in your DataFrame. Which of the following steps is required to implement this workflow effectively?
- A
Group the dataset by 'segment_id' and apply a custom function to train models for each group.
- B
Ensure the custom function returns a model or predictions for each group when applied.
- C
Use Databricks
pandas_udfto parallelize the group-specific model training across a cluster. - D
Manually split the dataset by 'segment_id' and train models sequentially for each segment.
- E
Convert the DataFrame to a Pandas DataFrame before applying group-specific operations.
Show answer and explanation
Correct answers: A, B, C
Explanation
The Pandas Function API enables users to perform group-specific operations on a dataset by grouping it by a key column (e.g., 'segment_id') and applying a custom function to each group. In Databricks, this can be parallelized using pandas_udf, which distributes the computation across a cluster for efficiency. The custom function should handle the training or prediction logic for each group and return the necessary output. This workflow avoids the inefficiencies of sequential processing or manual splitting of data.
- A. Correct.
Correct: Using the Pandas Function API involves grouping the dataset by a key column (e.g., 'segment_id') and applying a custom function to process each group independently.
- B. Correct.
Correct: The custom function applied to each group must return either a trained model or the predictions for that group, depending on the use case.
- C. Correct.
Correct: Databricks allows you to use
pandas_udfto distribute group-specific computations across a cluster for efficiency. - D. Incorrect.
Incorrect: Manually splitting the dataset and training models sequentially does not leverage the scalability and parallelization provided by the Pandas Function API or Databricks.
- E. Incorrect.
Incorrect: While the Pandas Function API can work with Pandas DataFrames, in Databricks, operations are typically performed on Spark DataFrames to leverage distributed computing.