Databricks Certified Machine Learning - Associate
Get started today
Ultimate access to all questions.
Comments
Loading comments...
Ultimate access to all questions.
You are working on a project that requires tuning hyperparameters for a machine learning model using Apache Spark. Your team has limited compute resources, and you need to parallelize the hyperparameter tuning process to speed up the process. Explain how you would use Hyperopt and SparkTrials to parallelize the tuning of hyperparameters.
A
To parallelize the hyperparameter tuning process using Hyperopt and SparkTrials, you would first define the search space for the hyperparameters and the objective function that evaluates the model's performance. Then, you would create a SparkTrials object and pass it to the hyperopt.fmin function along with the search space, objective function, and the number of parallel trials. The hyperopt.fmin function would then distribute the trials across the available Spark workers, allowing for parallel execution of the hyperparameter tuning process.
B
To parallelize the hyperparameter tuning process using Hyperopt and SparkTrials, you would first define the search space for the hyperparameters and the objective function that evaluates the model's performance. Then, you would create a SparkTrials object and pass it to the hyperopt.fmin function along with the search space, objective function, and the number of parallel trials. The hyperopt.fmin function would then distribute the trials sequentially across the available Spark workers, without any parallel execution.
C
To parallelize the hyperparameter tuning process using Hyperopt and SparkTrials, you would first define the search space for the hyperparameters and the objective function that evaluates the model's performance. Then, you would create a SparkTrials object and pass it to the hyperopt.fmin function along with the search space, objective function, and the number of sequential trials. The hyperopt.fmin function would then distribute the trials across the available Spark workers, allowing for parallel execution of the hyperparameter tuning process.
D
To parallelize the hyperparameter tuning process using Hyperopt and SparkTrials, you would first define the search space for the hyperparameters and the objective function that evaluates the model's performance. Then, you would create a SparkTrials object and pass it to the hyperopt.fmin function along with the search space, objective function, and the number of parallel trials. The hyperopt.fmin function would then distribute the trials across the available Spark workers, but the SparkTrials object would not enable parallelization.