Databricks Machine Learning Associate Question 321: You are tasked with tuning the hyperparameters of a Spark ML model... | Databricks Machine Learning Associate Dumps

Databricks Machine Learning Associate practice question 321 of 656

Databricks Certified Machine Learning Associate. Associate level, Databricks. Free question with the correct answer and a full explanation.

Databricks Machine Learning Associate Question 321

Select 2

You are tasked with tuning the hyperparameters of a Spark ML model using Hyperopt in Databricks. You want to parallelize the tuning process to efficiently utilize cluster resources. Which of the following steps are necessary to achieve this?

A
Define a search space for the hyperparameters using Hyperopt's hp module.
B
Use the fmin() function with the Trials class to log the tuning results.
C
Set the max_evals parameter in the fmin() function to a value greater than 1.
D
Set the sparkTrials argument in the fmin() function to an instance of SparkTrials.
E
Ensure the Spark ML model is transformed into a PySpark DataFrame before applying Hyperopt.

Show answer and explanation

Correct answers: A, D

Explanation

To parallelize hyperparameter tuning using Hyperopt and Spark ML in Databricks, it is necessary to define a search space for the hyperparameters using Hyperopt's hp module and use the SparkTrials class with the fmin() function. This allows the optimization process to distribute trials across the nodes of the Spark cluster, making full use of the available resources.

A. Correct.
Correct. Defining a search space using Hyperopt's hp module is essential for specifying the range of hyperparameters to optimize.
B. Incorrect.
Incorrect. While the Trials class can log tuning results, it does not enable parallelization. For parallelization, the SparkTrials class should be used.
C. Incorrect.
Incorrect. The max_evals parameter controls the number of trials but does not inherently enable parallelization.
D. Correct.
Correct. The sparkTrials argument in the fmin() function must be set to an instance of SparkTrials to parallelize the tuning process across a Spark cluster.
E. Incorrect.
Incorrect. Transforming a Spark ML model into a PySpark DataFrame is not relevant to the use of Hyperopt for hyperparameter tuning.

Databricks Machine Learning Associate practice question 321 of 656

Databricks Machine Learning Associate Question 321

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