An organization notices that its machine learning (ML) model is showing less accurate results over time due to changing customer behavior. What issue is this?

Explanation

Data drift occurs when the statistical properties of incoming data change over time, reducing the model's accuracy.  Overfitting happens when a model performs well on training data but poorly on new data, not necessarily due to long-term changes.  Hyperparameter tuning optimizaes a model but does not address changes in data distribution over time.  Feature engineering errors occur during data preprocessing.