I failed my first MLOps interview because I could explain model drift conceptually but couldn't…

describe how I'd detect it in a production system with 50 microservices.

Theory vs. practice. That gap is exactly what MLOps interviews test.

The questions that come up repeatedly: How do you detect model drift in production? (Hint: statistical tests on prediction distributions, not just accuracy monitoring.) Design a CI/CD pipeline for ML models. How would you handle A/B testing for ML? How do you version datasets AND models? How to scale model serving to 10K requests per second? Design a retraining pipeline.

What separates good answers from great ones: specifics. Don't say "I'd monitor for drift." Say "I'd use Evidently AI to compute PSI on prediction distributions weekly, with alerting thresholds set relative to baseline drift rates, and automatic rollback triggers."

The pattern I've noticed: companies don't want MLOps theorists. They want people who've actually been paged at 2 AM because a model started returning garbage and had to fix it.

If you can confidently walk through real production scenarios — including the messy parts — you'll stand out.