An IS auditor notes that the combined number of records in the training, validation, and...

In a proper AI pipeline, the original data must be " strictly partitioned " into disjoint sets. If the sum of the subsets exceeds the original count, it means some records were " Reused " across sets. This leads to " Data Leakage, " where the model " sees " the test or validation data during the training phase. As a result, the model ' s performance metrics will be artificially inflated, giving a false sense of accuracy. The ISACA AAIA™ Study Guide identifies data leakage as a primary cause of " Model Evaluation Failure, " rendering all testing results invalid.