Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 320 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 320

Select 3

You are working on a machine learning project in Databricks using Spark ML, and you need to optimize the hyperparameters of a RandomForestClassifier model. You decide to use Hyperopt for this task. Which of the following steps are required to parallelize the hyperparameter tuning process using Hyperopt and Trials in a distributed environment?

  1. A

    Define the hyperparameter search space using Hyperopt's hp module.

  2. B

    Use SparkTrials instead of Trials to enable parallel execution across the cluster.

  3. C

    Set the max_evals parameter in Hyperopt’s fmin() function to a value greater than the default.

  4. D

    Configure a distributed computing cluster in Databricks to execute the trials in parallel.

  5. E

    Ensure Hyperopt is used with a Pandas-based execution backend to support parallelism.

Show answer and explanation

Correct answers: A, B, D

Explanation

To parallelize the tuning of hyperparameters using Hyperopt in Databricks, you must define the search space using the hp module, use SparkTrials (which is designed for distributed execution), and ensure your Databricks environment is configured for distributed computing. These steps are essential for leveraging Hyperopt's parallel tuning capabilities in a Spark environment.

  • A. Correct.

    Defining the hyperparameter search space using Hyperopt's hp module is essential for specifying the range of values to explore during tuning. This is a required step for using Hyperopt.

  • B. Correct.

    Using SparkTrials instead of Trials is necessary to enable parallel execution of Hyperopt trials across a Databricks cluster. SparkTrials is specifically designed for distributed environments.

  • C. Incorrect.

    While setting the max_evals parameter to a higher value can allow more trials to be executed, it does not directly enable parallelism. This does not address the requirement to parallelize the process.

  • D. Correct.

    Configuring a distributed computing cluster in Databricks is a prerequisite for enabling parallelism when using SparkTrials. Without a properly set up cluster, Hyperopt cannot distribute the trials.

  • E. Incorrect.

    Hyperopt does not rely on a Pandas-based execution backend for parallelism. Instead, it uses SparkTrials for distributed parallel execution in a Spark environment.

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