AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

One of the first steps in the data preparation process before modeling is data cleaning. This step is critical because it ensures that the dataset is accurate, consistent, and free from errors that may compromise the integrity of the modeling process. Data cleaning involves identifying and correcting inaccuracies, removing duplicates, dealing with missing values, and addressing outliers.

By performing data cleaning at the outset, the model is built on a solid foundation of quality data, which enhances the reliability and predictive power of the model. Clean data helps algorithms perform better and avoid issues that may arise from garbage-in-garbage-out scenarios.

Other options, such as model fitting and model evaluation, occur later in the process once the data is prepared and ready to be used for training. Feature selection, while important, typically follows the cleaning phase. It involves choosing the most relevant features for modeling but assumes that the dataset has already been cleaned and organized. Thus, data cleaning is an essential first step in the overall data preparation process.