Databricks Machine Learning Associate Question 225
Single answerYou are working on hyperparameter tuning for a scikit-learn model using MLflow on Databricks. The model training process is taking a significant amount of time due to sequential execution of trials. How can you accelerate the hyperparameter tuning process using Databricks tools?
- A
Use SparkTrials to parallelize the execution of hyperparameter tuning trials across a Databricks cluster.
- B
Switch to a Spark MLlib model, as SparkTrials only supports distributed models.
- C
Enable AutoML in Databricks to automatically optimize the model without using SparkTrials.
- D
Run multiple MLflow experiments manually in parallel to achieve similar results as SparkTrials.
Show answer and explanation
Correct answer: A
Explanation
SparkTrials is a tool in Databricks MLflow that facilitates the parallel execution of hyperparameter tuning trials for single-node models like those in scikit-learn or XGBoost. By distributing the trials across the executors in a Databricks cluster, SparkTrials helps reduce the time required for hyperparameter optimization, especially for computationally intensive models. This makes it a practical choice for accelerating hyperparameter tuning workflows.
- A. Correct.
Correct. SparkTrials enables parallel execution of hyperparameter tuning trials across a Databricks cluster, significantly reducing tuning time for single-node models like those in scikit-learn.
- B. Incorrect.
Incorrect. SparkTrials is not limited to Spark MLlib models; it is specifically designed to parallelize single-node models like scikit-learn or XGBoost.
- C. Incorrect.
Incorrect. While AutoML can help automate model selection and tuning, it is not directly related to SparkTrials and does not provide trial parallelization for custom hyperparameter tuning workflows.
- D. Incorrect.
Incorrect. While running experiments manually in parallel might achieve similar results, it is not an efficient or scalable approach compared to using SparkTrials.