Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Explore fundamental principles of machine learning", feature engineering is the process used to generate additional features or transform existing data into forms that improve model performance. Features are individual measurable properties or characteristics used as input for machine learning algorithms. The goal of feature engineering is to create new informative variables that better represent the underlying patterns in the data.
Feature engineering may include tasks such as:
* Combining or transforming raw data columns (e.g., creating a "total purchase amount" from price × quantity).
* Extracting time-based components (e.g., year, month, day, hour) from datetime values.
* Encoding categorical variables (e.g., one-hot encoding or label encoding).
* Scaling or normalizing numerical features.
* Creating polynomial or interaction terms to capture complex relationships.
Microsoft's AI-900 learning material emphasizes that the process of preparing data for machine learning involves data cleaning, feature engineering, and feature selection. While feature selection is about choosing the most relevant features from the existing dataset, feature engineering focuses on creating or generating new features to enhance model accuracy and generalization.
The other options do not fit this definition:
* Feature selection is about removing redundant or irrelevant features, not generating new ones.
* Model evaluation involves assessing the model's performance using metrics like accuracy or F1 score.
* Model training is the phase where the algorithm learns patterns from the data, not when features are created.
Therefore, based on the AI-900 official concepts and Microsoft's documentation, the correct answer is Feature engineering, as it is the process specifically used to generate additional features that improve machine learning model performance and predictive capability.