IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

The main purpose of data normalization is to adjust values in the dataset to a common scale without distorting differences in the ranges of values. Normalization is particularly important when combining data from different sources or when preparing data for machine learning models, as it ensures that no single feature dominates due to its scale. For example, if one feature ranges from 0 to 1 and another from 1,000 to 10,000, without normalization, the model may give undue importance to the larger scale feature.

Normalization techniques, such as min-max scaling or z-score standardization, allow for features to be measured on the same scale, which can enhance the performance of algorithms, especially those that rely on the distance between data points, like k-nearest neighbors or support vector machines.

The other options highlight different concepts in data processing but do not accurately describe normalization's primary function. Instead of increasing variability or maintaining ratios, normalization focuses on achieving uniformity across different features. Similarly, while reducing the number of variables might be a part of feature selection, it does not relate to the objectives of normalization.