Google Cloud Professional Data Engineer Exam

If your ML models are not achieving satisfactory results due to insufficient representation of business goals, what should you focus on?

1. Train the model with more of similar data.

2. Perform L2 regularization.

3. Perform feature engineering to enhance the data.

4. Train the model with the same data but use more epochs.

The correct answer is: Perform feature engineering to enhance the data.

Focusing on feature engineering to enhance the data is crucial when ML models are not achieving satisfactory results due to insufficient representation of business goals. Feature engineering involves creating new input features from the existing data that can better capture the underlying patterns and relationships relevant to the business objectives. This can include transforming raw data, encoding categorical variables, combining features, or selecting only the most relevant features that contribute to improved model performance. When the features of the dataset do not align well with the specific business goals, models may struggle to make accurate predictions. By refining and enhancing the feature set, you can provide the model with a richer and more informative input that directly addresses the objectives of the project, leading to improved outcomes. While adding more data, applying L2 regularization, or increasing epochs can be beneficial in certain contexts, these approaches may not necessarily improve the model’s performance in terms of aligning with business goals. More data may not help if it's not representative or useful, regularization primarily helps reduce overfitting, and simply training for more epochs can lead to overfitting without addressing the root cause of the misalignment with business objectives. Thus, feature engineering directly influences the model's capacity to meet the specific needs of the business.