Azure Data Scientists Associate Practice Exam

Data normalization plays a vital role in machine learning by standardizing the range of independent variables or features within a dataset. This process ensures that all features contribute equally to the model's learning process, which is particularly important when the features have different units or scales. For example, if one feature represents height in centimeters and another represents weight in kilograms, their values would be on different scales.

When models, especially those that use distance calculations (like K-nearest neighbors) or gradient descent-based algorithms (like linear regression or neural networks), are applied to non-normalized data, those features with larger ranges can disproportionately influence the outcome. Normalization scales the features to a common range, typically between 0 and 1 or to a standard normal distribution (mean of 0 and standard deviation of 1). This scaling helps improve the convergence speed of algorithms and ensures that the model learns from all features without bias towards those with larger values.

The other options address different concepts in machine learning that do not directly relate to the purpose of normalization. While eliminating redundant data points can enhance data quality, simplifying model structures can lead to better interpretability, and increasing the dataset size is important for training robust models, none of these processes specifically address the need to standardize feature