Q1
Walk me through how you would approach diagnosing and resolving model drift in a production machine learning system that's been serving predictions for 18 months. What metrics would you monitor, and what remediation strategies would you implement?
Why they ask this:* They're assessing your understanding of production ML lifecycle challenges, monitoring practices, and practical experience maintaining model performance over time—a critical skill for mid-level engineers.
Q2
Describe a time you had to choose between multiple algorithms or architectures (e.g., XGBoost vs. neural networks, or batch vs. real-time inference). How did you evaluate trade-offs, and what factors influenced your final decision?
Why they ask this:* They want to understand your decision-making framework, ability to weigh technical trade-offs like latency vs. accuracy, computational cost vs. performance, and whether you consider business constraints alongside technical metrics.
Q3
Explain your approach to feature engineering for a tabular dataset with 500+ raw variables and high multicollinearity. What techniques would you use to reduce dimensionality and ensure features remain interpretable?
Why they ask this:* This tests practical data handling skills, understanding of statistical concepts (correlation, VIF), domain knowledge of dimensionality reduction techniques, and ability to balance model performance with interpretability—essential for data-driven ML work.
Q4
Design a machine learning pipeline for a real-time recommendation system that must serve predictions within 100ms. Walk through your choices for model architecture, feature storage, and serving infrastructure.