Google Professional Machine Learning Engineer
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In the context of designing a machine learning solution for a retail company aiming to predict customer churn, the team is discussing the importance of feature engineering. The dataset includes raw customer transaction data, demographic information, and interaction logs. Considering the need for high model accuracy, compliance with data privacy laws, and scalability for millions of customers, which of the following best describes the role and significance of feature engineering in this scenario? Choose the best option.
A
Feature engineering is primarily about selecting the most expensive computational resources to ensure the highest model performance.
B
Feature engineering involves the physical construction of data centers to store the processed features securely.
C
Feature engineering is the process of transforming raw data into meaningful features that can improve the model's ability to predict customer churn, while ensuring compliance and scalability.
D
Feature engineering is equivalent to model evaluation, focusing on assessing the performance of different machine learning algorithms.
E
Feature engineering includes both transforming raw data into meaningful features and selecting the most relevant features to enhance model performance and efficiency.