In data drift detection, what indicates a significant shift requiring an alert?

• A. A significant shift in distributions compared to baselines.
• B. Any increase in data volume alone.
• C. Missing values in a subset.
• D. Change in file name.

Answer: (A)

In data drift detection, the key signal is a significant shift in the data distributions compared to baselines. When the statistical properties of incoming data—such as means, variances, distribution shapes, or category proportions—diverge beyond what the historical data established, it suggests the data the model will see has changed in a way that could impact performance. This kind of distribution shift is what triggers alerts, often measured with drift metrics and thresholds to flag potential degradation and the need for model retraining or adjustment.

An increase in data volume alone doesn’t indicate drift; it’s just more data arriving. Missing values in a subset might point to data quality or collection changes, but it doesn’t by itself confirm a shift in the underlying distributions. A change in a file name is metadata and does not reflect changes in the data content, so it wouldn’t signal drift either.