Top 3 Retraining Interview Questions
Browse 3 real-world technical and behavioral interview questions about Retraining. Review scenarios, edge cases, and architectural best practices.
What is the difference between data drift and concept drift, and does it change what you do?
Data drift is a change in the distribution of the inputs; concept drift is a change in the relationship between inputs and the outcome. You can detect data drift from inputs alone, concept drift needs labels or a performance signal, and only concept drift necessarily invalidates what the model learned.
How do you decide when to retrain a model?
Retrain when there is evidence the learned relationship no longer holds and you have data that reflects the new one. A fixed schedule is a fallback for when you cannot measure that, not a decision, and a drift alert is as likely to mean an upstream pipeline broke as it is to mean the world moved.
Your labels arrive weeks after the prediction. How does that shape what you can build?
Label latency caps your retraining cadence at roughly the label delay, forces evaluation to run on a lagging window that is always partially incomplete, and pushes you towards proxy labels that arrive sooner. Proxies buy speed at the price of optimising something adjacent to the outcome you care about. It also connects training data to the point an interviewer is testing.