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Free CERTIFIED-MACHINE-LEARNING-ASSOCIATE – Databricks CERTIFIED-MACHINE-LEARNING-ASSOCIATE Practice Test Question

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Showing 1–3 of 5 questions

Question 1 (Topic 1)

A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block: A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model. Which of the following is a negative consequence of the approach suggested by the colleague?

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  • ○ The model will take longer to train for each unique combination of hyperparameter values
  • ○ The feature engineering stages will be computed using validation data
  • ○ The cross-validation process will no longer be parallelizable
  • ○ The cross-validation process will no longer be reproducible
  • ○ The model will be refit one more per cross-validation fold
Question 2 (Topic 1)

A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want to specify a search space for two hyperparameters and let the tuning process randomly select values for each evaluation. They attempt to run the following code block, but it does not accomplish the desired task: Which of the following changes can the data scientist make to accomplish the task?

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  • ○ Replace the GridSearchCV operation with RandomizedSearchCV
  • ○ Replace the GridSearchCV operation with cross_validate
  • ○ Replace the GridSearchCV operation with ParameterGrid
  • ○ Replace the random_state=0 argument with random_state=1
  • ○ Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')
Question 3 (Topic 1)

Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?

Select an option, then click Submit answer.

  • ○ TrainValidationSplit
  • ○ DataFrame.where
  • ○ CrossValidator
  • ○ TrainValidationSplitModel
  • ○ DataFrame.randomSplit