Databricks Machine Learning Associate Question 493: You are working on a machine learning project in Databricks and need... | Databricks Machine Learning Associate Dumps

Databricks Machine Learning Associate practice question 493 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 493

Select 4

You are working on a machine learning project in Databricks and need to preprocess a dataset stored in a Delta table for training. The dataset contains missing values in several columns, and you want to handle them effectively. Which of the following actions are appropriate for handling missing values during data processing in Databricks?

A
Use the fillna method to replace missing values with a default value.
B
Drop rows with missing values using the dropna method.
C
Use the filter method to remove rows with null values from specific columns.
D
Use the Spark SQL CASE statement to replace missing values with computed values.
E
Ignore the missing values and proceed directly to model training.

Show answer and explanation

Correct answers: A, B, C, D

Explanation

Handling missing values is a critical part of data preprocessing in machine learning workflows. In Databricks, you can use various tools such as fillna, dropna, filtering with conditions, and SQL-based transformations to address missing values effectively. Ignoring missing values is not recommended, as it can negatively affect model performance. Each of the correct options provides a valid technique for handling missing data in Databricks, depending on the specific requirements of the dataset and use case.

A. Correct.
Using the fillna method is a common approach to replace missing values in a DataFrame with default values and is supported in Databricks.
B. Correct.
The dropna method is a valid way to remove rows with missing values, especially when the proportion of missing data is small.
C. Correct.
The filter method can be used to explicitly filter out rows with null values by applying conditions on specific columns.
D. Correct.
Spark SQL supports the use of the CASE statement to replace missing values with computed or conditional values during a SQL query.
E. Incorrect.
Ignoring missing values without addressing them can lead to poor model performance, as models may not handle nulls effectively during training.

Databricks Machine Learning Associate practice question 493 of 656

Databricks Machine Learning Associate Question 493

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