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Columbia University AI/ML Analyst Interview Questions

The questions to prepare for a Columbia University AI/ML Analyst interview. Questions from real interview reports rank first. Updated daily.

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1
Machine LearningStart here. 7 questions · ~56 min
Handling Overfitting in Predictive ModelsMedium

Explain how to diagnose and reduce overfitting using regularization, validation strategy, and model complexity controls.

Cross-ValidationBias-Variance TradeoffRegularizationColumbia University
Supervised vs Unsupervised LearningEasy

Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.

Unsupervised LearningFeature EngineeringBias-Variance TradeoffColumbia University
Neural Network for Image RecognitionHard

Tests your end-to-end approach to designing, training, and validating a deep learning model for images.

Hyperparameter TuningNeural NetworksDeep LearningColumbia University
Feature Selection and ImportanceMedium

Tests your understanding of feature selection methods and their impact on model quality and generalization.

Cross-ValidationFeature EngineeringBias-Variance TradeoffColumbia University
Describe an ML Project OutcomeMedium

Tests your ability to deliver ML projects, measure impact, and communicate results clearly.

Cross-ValidationFeature EngineeringSupervised LearningColumbia University
Implement K-Means ClusteringMedium

Tests your ability to implement and reason about an unsupervised clustering algorithm.

Hyperparameter TuningUnsupervised LearningFeature EngineeringColumbia University
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2
More topics2 questions · ~16 min
Validate a Machine Learning ModelEasy

How to validate a machine learning model and interpret whether its metrics are trustworthy.

PrecisionAccuracyRecallColumbia University
Model Evaluation MetricsHard

Select appropriate metrics for evaluating a model, considering task type, error costs, class balance, and deployment goals.

performance evaluationmodel performanceevaluation metricsColumbia University

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