In a Spark MLlib implementation, you are working with a large dataset and need to perform data preprocessing to improve the quality of your machine learning model. Which of the following data preprocessing techniques can be applied in Spark MLlib, and how do they work?

Simulated

Data cleaning, which involves handling missing values, outliers, and errors in the dataset.

3.9%

Data transformation, which involves converting the data into a suitable format for machine learning models, such as normalization or standardization.

7.8%

Feature engineering, which involves creating new features from existing data to improve the model's performance.

3.9%

All of the above, as Spark MLlib supports various data preprocessing techniques.

84.4%

Databricks Certified Machine Learning - Associate