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

A data scientist is working with a feature set with the following schema:

The customer_id column is the primary key in the feature set. Each of the columns in the feature set has missing values. They want to replace the missing values by imputing a common value for each feature.
Which of the following lists all of the columns in the feature set that need to be imputed using the most common value of the column?

• A.customer_id, loyalty_tier
• B.loyalty_tier
• C.customer_id
• D.spend
• E.units

Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

• A.pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
• B.pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
• C.pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
• D.pandas API on Spark DataFrames are more performant than Spark DataFrames
• E.pandas API on Spark DataFrames are unrelated to Spark DataFrames

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?

• A.DataFrame.where
• B.TrainValidationSplitModel
• C.CrossValidator
• D.DataFrame.randomSplit
• E.TrainValidationSplit

A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library's fmin operation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with the objective_function being passed as an argument to fmin.
They use the following code block to create the objective_function:

Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?

• A.Add a random_state argument to the RandomForestRegressor operation
• B.Remove the mean operation that is wrapping the cross_val_score operation
• C.Replace the r2 return value with -r2
• D.Add test set validation process
• E.Replace the fmin operation with the fmax operation

The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

• A.Singular value decomposition
• B.Iterative optimization
• C.Logistic regression
• D.Least-squares method
• E.Spark ML cannot distribute linear regression training