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Advanced data drift for production ML: multivariate and embedding drift, MMD and domain classifiers, concept vs covariate shift under delay, causal pitfalls, and governance of retraining. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Operational data drift detection: PSI and statistical tests, feature vs prediction drift, windowing, alert design, and retraining triggers. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Foundations of data drift in ML: covariate shift, monitoring live inputs against a reference baseline, and why production distributions change. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
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