Problem
Scenario
You've shipped a model that looked strong in testing, but it is not holding up in production. You need to figure out why the offline results were misleading and how to get the model back to acceptable performance.
Question
Describe a situation where a model performed well in testing but failed in production. How did you diagnose and fix the issue?
What this tests
- Diagnosing offline versus production metric gaps
- Calibration and threshold drift
- Confusion matrix interpretation
- Validation design, especially cross-validation choices
Practicing as: AI Engineer interview at Lockheed MartinHi, I'll play your Lockheed Martin interviewer for the AI Engineer role. Candidates describe these interviews as mostly positive and moderately difficult, so expect me to be friendly and conversational. Take your time with the question above and answer like we're in the room.
You are practicing as a guest. Sign up free to get your answer graded with AI feedback. Your draft stays right here.
