A fraud model needs historical features as they existed at each transaction time. A join currently uses each customer latest feature values. What is required?

1. Remove event timestamps from the feature records.
2. Use event-time-aware point-in-time feature retrieval for the historical training rows.
3. Randomly assign feature snapshots to transaction rows.
4. Use the newest feature record for every historical transaction.