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Top 88 Machine Learning / AI Interview Questions & Answers (2026)

88 Questions 42 Beginner 30 Intermediate 16 Advanced

About Machine Learning / AI

Top 100 Machine Learning and AI interview questions covering supervised learning, neural networks, deep learning, NLP, and MLOps. Companies hiring for Machine Learning / AI roles test this knowledge at every stage — from first-round screens that check the basics to final-round conversations about real-world trade-offs.

What to Expect in a Machine Learning / AI Interview

Expect a mix of conceptual and practical Machine Learning / AI questions: clear definitions and core-concept checks for junior roles, hands-on scenario questions for mid-level roles, and architecture or trade-off discussions for senior roles. Interviewers usually move from foundational topics toward the kind of problems you'd actually face on the job.

How to Use This Guide

Work through the Machine Learning / AI questions in order — Beginner, then Intermediate, then Advanced — so each concept builds on the last. Every question has its own page; bookmark the ones that trip you up and revisit them the day before your interview.

Curated by Tech Baithak Editorial Team · Last updated: May 2026

Beginner 42 questions

Core concepts every Machine Learning / AI developer must know.

01 What is Machine Learning? 02 What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning? 03 What is supervised learning? 04 What is unsupervised learning? 05 What is a training set, validation set, and test set? 06 What is overfitting and underfitting? 07 What is a feature in machine learning? 08 What is a label or target variable? 09 What is classification vs regression? 10 What is a neural network? 11 What is gradient descent? 12 What is a loss function? 13 What is an activation function? 14 What is cross-validation? 15 What is the bias-variance tradeoff? 16 What is logistic regression? 17 What is linear regression? 18 What is a decision tree? 19 What is a Random Forest? 20 What is k-Nearest Neighbors (KNN)? 21 What is a Support Vector Machine (SVM)? 22 What is feature scaling and why is it important? 23 What is the difference between parameters and hyperparameters? 24 What is regularization? 25 What is a confusion matrix? 26 What is precision, recall, and F1-score? 27 What is accuracy and when is it misleading? 28 What is the ROC curve and AUC? 29 What is K-Means clustering? 30 What is Principal Component Analysis (PCA)? 31 What is transfer learning? 32 What is data augmentation? 33 What is batch size and how does it affect training? 34 What is an epoch? 35 What is the learning rate? 36 What is backpropagation? 37 What are the main types of machine learning algorithms? 38 What is Naive Bayes? 39 What is the curse of dimensionality? 40 What is a hyperparameter search? 41 What is a pipeline in machine learning? 42 What is data leakage in machine learning?

Intermediate 30 questions

Practical knowledge for developers with hands-on experience.

01 What is a convolutional neural network (CNN)? 02 What is a Recurrent Neural Network (RNN)? 03 What is an LSTM and how does it solve the vanishing gradient problem? 04 What is the attention mechanism in neural networks? 05 What is the Transformer architecture? 06 What is BERT and how is it pre-trained? 07 What is GPT and how does it differ from BERT? 08 What is word embedding and what is Word2Vec? 09 What is a Generative Adversarial Network (GAN)? 10 What is reinforcement learning? 11 What is an autoencoder? 12 What is dropout regularization? 13 What is batch normalization? 14 What is gradient vanishing and exploding? 15 What is an ensemble method? 16 What is XGBoost and why is it popular? 17 What is natural language processing (NLP)? 18 What is semantic segmentation vs object detection? 19 What is the difference between classification and clustering? 20 What is the softmax function? 21 What is a ResNet (Residual Network)? 22 What is fine-tuning a pre-trained model? 23 What is the difference between parametric and non-parametric models? 24 What is DBSCAN clustering? 25 What is class imbalance and how do you handle it? 26 What is a recommendation system? 27 What is t-SNE? 28 What is the difference between discriminative and generative models? 29 What is Bayesian inference in machine learning? 30 What is MLOps?

Advanced 16 questions

Deep expertise questions for senior and lead roles.

01 What is the Transformer self-attention mechanism in detail? 02 What is RLHF (Reinforcement Learning from Human Feedback)? 03 What is the difference between model parallelism and data parallelism? 04 What is a diffusion model? 05 What is LoRA (Low-Rank Adaptation)? 06 What is knowledge distillation? 07 What is neural architecture search (NAS)? 08 What is contrastive learning and what is SimCLR? 09 What is quantization in deep learning? 10 What is the concept of model interpretability and explainability? 11 What is federated learning? 12 What is concept drift and how is it handled? 13 What are graph neural networks (GNNs)? 14 What is the mixture of experts (MoE) architecture? 15 What is position encoding in Transformers? 16 What is the role of the KV cache in LLM inference?

Back to All Topics 88 questions total