314,552 interview questions from 6,000+ companies.
Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.
Explain practical strategies for handling missing values in a supervised learning workflow, from diagnosis to modeling and validation.
Explain how to reduce overfitting using regularization, validation, and model selection.
Explain the bias-variance tradeoff and how it guides model choice, regularization, and generalization performance.
Pick metrics for a new program by tying them to the goal, separating leading and lagging signals, and defining a clear KPI set.
Explain how to design and evaluate an A/B test for a product feature, including metrics, MDE, sample size, and guardrails.
Define the core metrics for a new product launch, from early adoption and activation to retention and long-term value.
Decide which customer segment should get a new product improvement first.
Build a classifier for a highly imbalanced dataset and choose metrics, sampling, and thresholds that fit the minority class.
Explain how decision trees split data, make predictions, and trade interpretability against overfitting.
Explain how to choose and evaluate a predictive model, then connect the output to a business decision.
