How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at Amazon.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Amazon during technical interviews.

Explain ML basics: imbalance, metrics, bias-variance

Quick Overview

This question evaluates proficiency in handling class imbalance, selecting evaluation metrics, reasoning about bias–variance trade-offs, and comparing model inductive biases (CNNs vs. Transformers) within supervised Machine Learning for a Machine Learning Engineer role.

Handling Class Imbalance, Bias–Variance, Metrics, and Model Choices

Context

You are building a supervised classifier for a highly imbalanced task (e.g., fraud detection) where the positive class is rare. Discuss how you would:

Diagnose class imbalance.
Address imbalance using data-level and algorithm-level techniques (e.g., over/under-sampling, SMOTE variants, class-weighted losses, focal loss), data augmentation, threshold tuning, and probability calibration.
Design validation to avoid leakage and to reflect real class priors.
Define the bias–variance trade-off and outline concrete steps for high bias vs. high variance (model capacity, regularization, features, data).
Choose appropriate evaluation metrics for imbalanced data and justify them—contrast accuracy, ROC-AUC, PR-AUC, F1/Fβ, recall@k/precision@k, and expected business cost; explain when each is preferable.
Briefly explain the core ideas and inductive biases of CNNs vs. Transformers and when you would prefer each for text, images, sequences, or tabular data.

Explain ML basics: imbalance, metrics, bias-variance

Quick Overview

Handling Class Imbalance, Bias–Variance, Metrics, and Model Choices

Context

Solution

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