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To efficiently parallelize the training of multiple machine learning models with different configurations in a PySpark job on Databricks, what is the best approach?
A
Implement multi-threading within a single Spark job for concurrent model training.
B
Submit multiple Spark jobs concurrently using Databricks Jobs.
C
Use Spark‘s built-in parallelization feature for DataFrame operations.
D
Leverage Spark MLlib‘s parallel training capabilities for ensemble models.
