ML interview questions
A/B Testing & Experimentation
Experiment design, peeking, multiple comparisons, novelty and network effects, switchback, CUPED, the offline–online gap, the OEC and A/A tests.
12 questions
AI Agents & LLM Evaluation
LLM-as-judge and evaluation, guardrails, prompt injection, tool calling, the ReAct loop, hallucination and structured output.
11 questions
Association Rules
Market-basket analysis: support, confidence and lift, and how the Apriori property prunes the itemset search.
5 questions
Multi-Armed Bandits
Online decisions under uncertainty: exploration versus exploitation, epsilon-greedy, UCB, and when a bandit beats a fixed A/B split.
6 questions
Gradient Boosting in Practice
Implementation specifics of XGBoost, LightGBM and CatBoost: tree-growth strategies, histogram splits, GOSS/EFB, ordered target statistics, monotone constraints and hyperparameters.
14 questions
Computer Vision
Convolutional and pooling layers, classic CNN architectures (Inception, ResNet residual blocks), and test-time augmentation.
20 questions
Decision Trees
Single-tree mechanics: split criteria, impurity, pruning, extrapolation limits, feature-importance bias, and native missing-value handling.
13 questions
Deep Learning Training
Neural-network regularization, dropout at train vs inference, normalization layers (BatchNorm/LayerNorm), gradient accumulation, and set/graph architectures.
24 questions
Dimensionality Reduction: LDA and Kernel PCA
Reducing features beyond plain PCA: supervised LDA and its component limit, and kernel PCA for non-linear structure.
3 questions
Ensembles
Bagging and Random Forest, gradient boosting and GBDT implementations, the bias/variance effect, hyperparameter tuning, and stacking.
21 questions
Feature Engineering
Encoding, scaling, imputation, outliers, transforms, feature selection, multicollinearity and leak-free temporal features.
14 questions
Generalization & Validation
Bias–variance, the curse of dimensionality, double descent, train/validation/test discipline, cross-validation schemes and the four faces of data leakage.
14 questions
Imbalanced Data
Strategies for class imbalance: resampling, SMOTE, class weights, threshold selection, the anomaly-vs-classification framing and calibration after resampling.
8 questions
k-Nearest Neighbors
Distance-based prediction: choosing k, distance metrics, why feature scaling is mandatory, and the cost of a lazy learner.
4 questions
LLM Architecture
Transformer internals for large language models: autoregressive generation, the KV cache, MQA/GQA, RoPE, Mixture-of-Experts, tokenizer training and scaling laws.
10 questions
LLM Serving & Inference
Quantization, KV-cache memory, speculative decoding, FlashAttention, continuous batching, decoding strategies, latency vs throughput and serving cost.
11 questions
LLM Training & Adaptation
Fine-tuning vs RAG vs prompting, LoRA/QLoRA, catastrophic forgetting, SFT/RLHF/DPO alignment, instruction tuning and prompting strategies.
11 questions
Linear Models & Optimization
Linear regression, regularization (L1/L2/Elastic Net), the closed-form OLS solution and its pitfalls, and gradient-based optimization.
30 questions
Machine Learning Basics
What machine learning is: supervised and unsupervised learning, classification versus regression, features and targets, and the fit/predict workflow.
4 questions
MLOps & Production ML
Training-serving skew, data vs concept drift, feature stores, retraining triggers, reproducibility, model registries and production monitoring.
14 questions
ML System Design
End-to-end design of recommender, fraud, search-ranking, CTR, moderation, dedup, churn, chatbot, matcher and ETA systems, from metric to serving cost.
15 questions
Quality Metrics
Precision, recall and accuracy, the precision/recall threshold trade-off, AUC-ROC and its rank-based properties, and regression metrics under outliers.
20 questions
NLP & Transformers
Word and text embeddings, the Transformer encoder/decoder, self-attention and positional encoding, and BERT for embeddings and question answering.
21 questions
Naive Bayes
Probabilistic classifiers built on Bayes' theorem: the independence assumption, Gaussian versus multinomial variants, and Laplace smoothing.
3 questions
NumPy and pandas
The data tools under every ML pipeline: NumPy arrays, shapes, axes and broadcasting, pandas DataFrames, missing values and grouping.
5 questions
Probability & Statistics for ML
Maximum likelihood, Bayes' theorem, correlation vs independence, the CLT, p-values, distributions, confidence intervals and sample-size/power.
14 questions
Retrieval-Augmented Generation
RAG pipeline anatomy, chunking strategies, hybrid search, vector databases and HNSW, grounding/refusal, and separating retrieval from generation quality.
9 questions
Recommender Systems
Collaborative/content/hybrid filtering, matrix factorization, cold start, candidate generation + ranking, two-tower models, NDCG, position bias and feedback loops.
11 questions
Responsible ML
Shipping models people can trust: bias through proxy features, group fairness checks, and permutation importance for explainability.
2 questions
Support Vector Machines
Maximum-margin classifiers and regressors: support vectors, soft margin C, the kernel trick, RBF gamma and the SVR epsilon-tube.
5 questions
Time Series Forecasting
Stationarity and the ADF test, ACF/PACF, rolling-origin backtesting, GBDT vs ARIMA, multi-step forecasting, seasonality and intermittent demand.
11 questions
Unsupervised Learning
k-means and DBSCAN clustering, PCA and LDA, t-SNE/UMAP, and autoencoder-based anomaly detection.
17 questions
