Google Cloud Professional Machine Learning Engineer Practice Test - Prep & Practice Exam

The preprocessing method that involves using a hash function is hashing. Hashing transforms input data into fixed-size numerical values through a mathematical function called a hash function. This method is particularly useful for handling high-dimensional categorical data or when the dataset has a large number of unique values. By hashing, you can effectively reduce memory usage and increase computational efficiency, enabling faster model training and inference, particularly in situations with large datasets.

In contrast, the other options represent different data preprocessing techniques that do not directly utilize hash functions. Encoding typically involves transforming categorical variables into numerical formats using techniques like one-hot encoding or label encoding. Normalization is the process of scaling numerical features to fall within a specific range, often used to improve model convergence. Vectorization is the conversion of text data into numerical representations, like using term frequency-inverse document frequency (TF-IDF) or word embeddings, but does not specifically involve hashing. Each of these techniques serves distinct purposes and is chosen based on the specific characteristics of the data being processed.