A Machine Learning Engineer uses Lakehouse Monitoring to track their credit scoring model's performance. The existing profile metrics table contains three aggregate metrics:
- adefault_risk_score
- payment_history_score
- credit_utilization_score
They need to:
1. Create a composite risk rating that combines these three scores using weights of 0.5, 0.3, and 0.2 respectively.
2. Monitor drift of this composite score against an established baseline.
Which approach should be used to implement both requirements within Lakehouse Monitoring?