TD Interview Questions
The questions to prepare for TD interviews, across all roles. Questions from real interview reports rank first. Updated weekly.
Explain how bagging and boosting differ, and identify a representative algorithm for each ensemble method.
Explain how to train and evaluate models on highly imbalanced fraud data without relying on misleading accuracy.
Explain what a p-value means, how it relates to statistical significance, and how to describe it clearly to non-technical stakeholders.
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Design a production ML deployment on Google Cloud with serving, feature management, rollout, monitoring, and evaluation.
Explain which metrics matter for evaluating a churn model and how to choose them based on retention costs and business goals.
Design an A/B test for a new platform feature, including success metrics, power, guardrails, and a clear ship decision.
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