Databricks Certified Machine Learning - Associate
Get started today
Ultimate access to all questions.
Comments
Loading comments...
Ultimate access to all questions.
A data scientist is attempting to use Spark ML to impute missing values in their PySpark DataFrame 'features_df'. The goal is to replace missing values in all numeric columns with the median of each respective column. However, the provided code snippet does not achieve this. What is the primary reason the code fails to perform the intended imputation? Choose the most accurate option._
A
Median value imputation is not supported in Spark ML.
B
The code does not apply imputation to both training and test datasets at the same time.
C
The 'inputCols' and 'outputCols' parameters must be identical for the imputation to work.
D
The imputer is not fitted to the data to create an 'ImputerModel' before transformation.
E
The 'fit' method should be used in place of 'transform' for imputation.