If the combined size of the training, validation, and testing sets exceeds the original data size, it suggests that records may have been reused across sets. This can lead to data leakage, where the model has access to test or validation information during training, resulting in overly optimistic performance metrics.
"Data leakage invalidates model evaluation because it introduces unintended data overlap. Auditors must ensure that the training, validation, and test sets are strictly partitioned." Options A, C, and D refer to process order or quantity, but only B addresses the root issue of compromised model integrity due to overlapping data.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Partitioning and Leakage Risks"