Azure Data Scientists Associate Practice Exam

Normalization is a technique applied in data preparation to:

1. Enhance the model training speed

2. Ensure all features have the same scale

3. Increase the number of features in the dataset

4. Reduce the dimensionality of data

The correct answer is: Ensure all features have the same scale

Normalization is a technique applied in data preparation to ensure that all features have the same scale. This process is important because many machine learning algorithms perform better when the input features are on a similar scale. For instance, models that calculate distances, like k-nearest neighbors or support vector machines, can be significantly impacted by the range of the input features. If one feature ranges between 0 and 1 while another ranges from 0 to 1,000, the latter can dominate the distance calculations, potentially leading to skewed or incorrect results. By applying normalization, such as Min-Max scaling or Z-score standardization, the features are transformed to a common scale, allowing the model to better interpret the relationship between them. This equal scaling aids in improving the convergence speed of gradient descent algorithms, and it also helps to avoid numerical instability. The other choices, while they describe different data preparation techniques or aspects, do not accurately describe the primary purpose of normalization. For example, enhancing model training speed relates more to optimization techniques rather than scaling features; increasing the number of features and reducing dimensionality pertain to feature engineering and selection techniques. Thus, ensuring that all features have the same scale is the essential role of normalization in data preparation.