Online Access Free Databricks-Machine-Learning-Associate Practice Test

A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrame train_df to train the model.
The Spark DataFrame train_df has the following schema:

The machine learning engineer shares the following code block:

Which of the following changes does the machine learning engineer need to make to complete the task?

• A.They need to utilize a Pipeline to fit the model
• B.They need to convert the features column to be a vector
• C.They need to split the features column out into one column for each feature
• D.They need to call the transform method on train df
• E.They do not need to make any changes

A data scientist is developing a machine learning pipeline using AutoML on Databricks Machine Learning.
Which of the following steps will the data scientist need to perform outside of their AutoML experiment?

• A.Model tuning
• B.Exploratory data analysis
• C.Model evaluation
• D.Model deployment

An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion?

• A.One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
• B.One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
• C.One-hot encoding is dependent on the target variable's values which differ for each apaplication.
• D.One-hot encoding is not a common strategy for representing categorical feature variables numerically.

A team is developing guidelines on when to use various evaluation metrics for classification problems. The team needs to provide input on when to use the F1 score over accuracy.

Which of the following suggestions should the team include in their guidelines?

• A.The F1 score should be utilized over accuracy when identifying true positives and true negatives are equally important to the business problem.
• B.The F1 score should be utilized over accuracy when the number of actual positive cases is identical to the number of actual negative cases.
• C.The F1 score should be utilized over accuracy when there are greater than two classes in the target variable.
• D.The F1 score should be utilized over accuracy when there is significant imbalance between positive and negative classes and avoiding false negatives is a priority.

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?

• A.Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')
• B.Replace the random_state=0 argument with random_state=1
• C.Replace the GridSearchCV operation with RandomizedSearchCV
• D.Replace the GridSearchCV operation with cross_validate
• E.Replace the GridSearchCV operation with ParameterGrid