Databricks Machine Learning Associate Question 238: You are tasked with building a machine learning model in Databricks... | Databricks Machine Learning Associate Dumps

Databricks Machine Learning Associate practice question 238 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 238

Select 3

You are tasked with building a machine learning model in Databricks to predict customer churn. To ensure the model generalizes well to unseen data, you decide to perform k-fold cross-validation. Which of the following steps are necessary to implement k-fold cross-validation in Databricks?

A
Split the dataset into k equally sized folds and iterate through each fold as the validation set while training on the remaining folds.
B
Use the CrossValidator class from the pyspark.ml.tuning module to perform cross-validation.
C
Ensure that hyperparameter tuning is performed before cross-validation to speed up training.
D
Specify an evaluation metric, such as accuracy or RMSE, when using cross-validation.
E
Manually shuffle the dataset before splitting it into folds to ensure randomness.

Show answer and explanation

Correct answers: A, B, D

Explanation

To perform k-fold cross-validation in Databricks, the dataset is split into k folds, and the CrossValidator class is typically used to automate the process. You must also specify an evaluation metric to assess model performance. Hyperparameter tuning is usually integrated with cross-validation, and manual shuffling is unnecessary when using built-in tools like CrossValidator.

A. Correct.
Correct: This is the fundamental process of k-fold cross-validation, where the dataset is split into k folds, and each fold is used as the validation set once.
B. Correct.
Correct: The CrossValidator class in Spark MLlib automates the cross-validation process, integrating it with model training and evaluation.
C. Incorrect.
Incorrect: Hyperparameter tuning is often performed during cross-validation, not before it. Cross-validation helps evaluate different hyperparameter combinations efficiently.
D. Correct.
Correct: An evaluation metric must be specified to assess the model's performance during cross-validation and select the best model.
E. Incorrect.
Incorrect: Shuffling is generally handled automatically by cross-validation implementations like the CrossValidator class in Databricks, so manual shuffling is not required.

Databricks Machine Learning Associate practice question 238 of 656

Databricks Machine Learning Associate Question 238

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