Given a scenario where you need to perform feature engineering on a dataset that is distributed across multiple nodes, describe how you would approach this task using Spark ML. What specific features of Spark ML would you utilize and why?

Simulated

Use Spark ML's DataFrame API for feature extraction due to its simplicity.

14.3%

Leverage Spark ML's feature transformation functions like Bucketizer and VectorAssembler for efficient distributed processing.

85.7%

Implement custom Python scripts for feature engineering to ensure flexibility.

0.0%

Rely on manual data aggregation and then apply scikit-learn for feature engineering.

0.0%

Databricks Certified Machine Learning - Associate