Databricks Machine Learning Associate Question 219
Select 4You are training a machine learning model on Databricks and want to optimize its hyperparameters using Hyperopt. To speed up the tuning process, you decide to parallelize the search using SparkTrials. Which of the following steps are necessary to correctly implement this approach?
- A
Define the search space using Hyperopt's
hpmodule. - B
Use
SparkTrialsinstead ofTrialswhen running the Hyperopt tuning function. - C
Set the
parallelismparameter inSparkTrialsto specify the number of concurrent trials. - D
Manually distribute the data across Spark worker nodes before running the tuning job.
- E
Ensure the Databricks cluster has sufficient worker nodes to handle the specified parallelism.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
To parallelize hyperparameter tuning with Hyperopt and SparkTrials, you need to define a search space, use SparkTrials instead of Trials, and configure parallelism appropriately. Spark handles data distribution automatically, so no manual intervention is required. Additionally, the Databricks cluster must have sufficient resources to support the specified level of parallelism.
- A. Correct.
Correct. Defining the search space using Hyperopt's
hpmodule is a required step for any Hyperopt optimization, including when using SparkTrials. - B. Correct.
Correct. When using Hyperopt on Databricks with Spark, you must use
SparkTrialsinstead of the defaultTrialsto enable parallel execution. - C. Correct.
Correct. The
parallelismparameter inSparkTrialsdetermines the number of trials that can run concurrently, which is key for parallelization. - D. Incorrect.
Incorrect. The data distribution is automatically handled by Spark when using
SparkTrials, so manual distribution is not necessary. - E. Correct.
Correct. The cluster must have enough worker nodes available to handle the level of parallelism specified, or the tuning process may be bottlenecked.