Your question is Evaluating Sparse or Delayed Ground Truth. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you evaluate the performance of a model when the ground truth is sparse or delayed?
Explain how you would separate model quality from label-availability bias, select interim metrics, and validate results once outcomes arrive. Address monitoring, delayed-label backtesting, uncertainty, sampling, and decisions about deployment or retraining.
