A training dataset includes fields that would only become available after the prediction outcome. Evaluation accuracy looks unrealistically high. Which approach best fits?

1. Profile the source data and compare results with expected constraints.
2. Validate referential relationships and quarantine or resolve orphaned records.
3. Add business-rule validation for value range and currency consistency.
4. Remove target leakage and rebuild an appropriately timed evaluation split.