Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

Normalization of features primarily ensures that all features have the same scale, which is essential for many machine learning algorithms. When feature values can vary significantly in scale, algorithms such as gradient descent can struggle to converge efficiently. This is because features with larger scales can disproportionately influence the optimization process, potentially leading to suboptimal solutions or slow learning.

By normalizing features, each feature is adjusted to a common scale, typically ranging between 0 to 1 or having a mean of zero and a standard deviation of one (in the case of standardization). This scaling allows models to treat all features equally, which can result in improved model performance, faster convergence, and reduced sensitivity to the initial conditions of the optimization algorithm.

While normalization can potentially improve computational efficiency as a secondary effect, its primary purpose is to ensure all features are comparable in scale. Other options do not correctly capture the role of normalization in the context of feature preparation for machine learning. Redundant feature elimination and complexity increase are unrelated to the scaling aspect addressed by normalization.