Model performance deterioration caused by shifts in the input data distribution is correct and this phrase describes the phenomenon known as data drift.

Data drift means the statistical properties of the inputs change between training and production and that shift can reduce a model's accuracy over time. Teams need to monitor input distributions, compare them to training distributions, and respond with alerts, data fixes, or retraining so models remain reliable in production.

Changes in the relationship between features and target labels over time is incorrect because that describes concept drift and not data drift. Concept drift refers to the underlying mapping from inputs to labels changing and it is a related but distinct problem.

Vertex AI Model Monitoring is incorrect because it names a monitoring product rather than defining the phenomenon. The service can help detect data drift, but it is not the definition of data drift itself.

The reformatting and cleaning of raw inputs so they are suitable for training is incorrect because that describes data preprocessing and feature engineering. Those tasks prepare data for modeling and do not refer to changes in production data distributions over time.

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