Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 94 discussion

D. Measure class imbalance on the training dataset. Adapt the training process accordingly: This is the correct answer. Class imbalance occurs when one class (e.g., loan approval) is significantly more represented in the training data than another. This can lead to biased models that favor the majority class. Measuring and addressing class imbalance (e.g., through resampling or weighting techniques) is crucial for building fair models. Why not B? B. Ensure that the ML model predictions are consistent with historical results: If historical results reflect existing biases in lending practices, ensuring consistency with them will simply perpetuate those biases. This is the opposite of what is desired.

The correct answer is D. Measuring and addressing class imbalance in training data is essential for developing unbiased ML models.

D. Measure class imbalance on the training dataset. Adapt the training process accordingly. Explanation: To develop an unbiased ML model, it is crucial to ensure that the training dataset represents all demographic groups fairly and that the model is not influenced by biases in the data.