Machine Learning interview questions

Showing 31–60 of 572 results

• Explain Collaborative Filtering ApproachesHard

  This question evaluates understanding of collaborative filtering approaches and related competencies such as neighborhood-based versus matrix factorization methods, differences for explicit versus implicit feedback, pointwise versus pairwise loss trade-offs, regularization, cold-start and popularity-bias mitigation, evaluation metrics, and scalability considerations. It is asked in the Machine Learning domain to assess both conceptual understanding and practical application for designing, evaluating (e.g., NDCG, MAP) and scaling recommender systems in technical interviews.

  AmazonApplied ScientistMachine LearningOnsite
• Explain Core ML Interview ConceptsHard

  This question evaluates core machine learning fundamentals including statistical modeling assumptions and loss functions (linear and logistic regression), ensemble methods and feature sampling in random forests, optimization algorithms (Adam versus stochastic gradient descent), and neural network capacity and training dynamics.

  AmazonApplied ScientistMachine LearningTechnical Screen
• Explain Decoder-Only LLM Architecture and How vLLM Serves It EfficientlyMedium

  A two-part conversational question for AI infrastructure roles: explain how a decoder-only transformer turns a prompt into next-token probabilities, then explain how vLLM serves such models efficiently. It tests transformer internals, KV cache sizing, prefill versus decode bottlenecks, paged KV memory and continuous batching.

  AmazonSoftware EngineerMachine LearningOnsite
• Explain K-Fold Cross-Validation and Its Trade-OffsMedium

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain K-Fold Cross-Validation and Its Trade-Offs states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonData ScientistMachine LearningTechnical Screen
• Explain LLM architecture, tuning, evaluationMedium

  This question evaluates understanding of transformer-based LLM architectures, positional embedding variants, parameter-efficient fine-tuning (PEFT) approaches, regularization strategies, and evaluation methodologies within the Machine Learning domain focused on large language models and NLP, emphasizing both conceptual understanding and practical application. It is commonly asked to assess the interviewee’s ability to reason about architecture and tuning trade-offs, model generalization and stability, and the design of reliable offline and online evaluation pipelines for production-minded language model systems.

  AmazonApplied ScientistMachine LearningTechnical Screen
• Explain Layer Normalization in TransformersMedium

  This question evaluates a candidate's understanding of normalization techniques in deep learning—specifically Layer Normalization in Transformer blocks—covering the LayerNorm equation and the roles of gamma, beta, and epsilon, as well as contrasts with BatchNorm and RMSNorm, in the Machine Learning domain.

  AmazonApplied ScientistMachine LearningOnsite
• Explain Logistic Regression FundamentalsHard

  This question evaluates understanding of logistic regression fundamentals, covering probabilistic modeling, Bernoulli likelihood and logit-link derivation, loss and gradient characterization, calibration and miscalibration remedies, regularization trade-offs, feature scaling and interactions, class imbalance strategies, evaluation metrics for skewed data, coefficient interpretation, and common failure modes. It is commonly asked in technical interviews to assess both theoretical and practical competencies in supervised learning within the Machine Learning domain, with a level of abstraction spanning conceptual derivation and practical application of model behavior and evaluation.

  AmazonApplied ScientistMachine LearningOnsite
• Explain ML evaluation, sequence models, and optimizersMedium

  This question evaluates a candidate's competency in machine learning model evaluation and metrics, sequence modeling trade-offs between transformers and RNNs, image distributional shift detection, and comparative understanding of optimization algorithms within the Machine Learning domain.

  AmazonApplied ScientistMachine LearningOnsite
• Explain NLP/RL concepts used in LLM agentsHard

  This question tests the ability to synthesize concepts across LLM agent architecture, RAG systems, and reinforcement learning, focusing on architectural design trade-offs and decision-making logic.

  AmazonApplied ScientistMachine LearningOnsite
• Explain Overfitting and Underfitting in Machine LearningMedium

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain Overfitting and Underfitting in Machine Learning states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonData ScientistMachine LearningTechnical Screen
• Explain XGBoost Parallelism StrategiesMedium

  This question evaluates a candidate's understanding of parallelism and system-level performance in gradient-boosted tree implementations, covering concepts such as histogram-based split finding, sparse feature handling, cache-friendly data layouts, thread-level work partitioning, and multi-machine data-parallel synchronization.

  AmazonApplied ScientistMachine LearningOnsite
• Explain core ML concepts and diagnosticsMedium

  This question evaluates mastery of core Machine Learning concepts and diagnostics, covering statistical inference (p-values), overfitting/underfitting and bias-variance, causal inference approaches, encoding/decoding, optimization and backpropagation, gradient stability, handling imbalanced data, evaluation metrics versus accuracy, and experimental A/B testing. It is commonly asked in ML interviews to assess breadth and depth in the Machine Learning domain and probes both conceptual understanding and practical application by testing theoretical knowledge, recognition of common failure modes, and diagnostic reasoning used to validate models and experiments.

  AmazonApplied ScientistMachine LearningTechnical Screen
• Explain imbalance, metrics, bias-variance, Transformers vs. CNNsHard

  An Amazon machine-learning-engineer technical screen covering class-imbalance handling at the data, loss, and decision levels; leakage-aware validation; the bias–variance tradeoff; metric selection for rare-positive fraud data (PR-AUC, precision@k, calibration); and Transformer vs. CNN inductive biases. Includes worked numeric examples for cost-based thresholds and metric interpretation.

  AmazonApplied ScientistMachine LearningTechnical Screen
• Explain key ML theory and techniquesHard

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain key ML theory and techniques states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonApplied ScientistMachine LearningOnsite
• Explain overfitting, regularization, and LLM techniquesMedium

  This question tests the conceptual understanding of model generalization trade-offs, the mathematical mechanics of regularization, and the practical application of modern LLM architectures.

  AmazonSoftware EngineerMachine LearningTechnical Screen
• Explain surprisal and its unitsMedium

  This question evaluates understanding of probabilistic information measures in language modeling, specifically the concept of surprisal, its units, and its relationship to evaluation metrics such as cross-entropy and perplexity within the Machine Learning/NLP domain.

  AmazonApplied ScientistMachine LearningTechnical Screen
• Explain why LASSO selects featuresMedium

  This question evaluates understanding of regularization and feature selection in linear models, covering competencies in LASSO's L1 penalty versus L2, geometric constraint intuition, optimality/KKT conditions, effects of correlated predictors, the role of standardization, hyperparameter selection, and when Elastic Net is appropriate, within the Machine Learning domain for Data Scientist roles. It is commonly asked because it probes both conceptual understanding and practical application of model sparsity, interpretability, preprocessing, and bias–variance trade-offs, testing knowledge of statistical optimization and model selection rather than implementation details.

  AmazonData ScientistMachine LearningTechnical Screen
• From Early NLP Embeddings to Modern LLM EmbeddingsMedium

  Evaluate the architectural and practical evolution of NLP text embeddings, and formulate a decision framework comparing small bi-encoders versus large LLM-based embedding models based on retrieval quality, latency, and operational cost.

  AmazonApplied ScientistMachine LearningOnsite
• GRPO Deep Dive: Critic-Free RL, Parallelism, MLA, and Reward Design for a Reasoning LLMHard

  This question tests your deep understanding of Group Relative Policy Optimization (GRPO) for post-training reasoning models, including how critic-free optimization alters training dynamics, distributed memory scaling, KV-cache efficiency via Multi-Head Latent Attention (MLA), and reward hacking mitigations.

  AmazonApplied ScientistMachine LearningTechnical Screen
• GRPO in NumPy: Group-Relative Advantages, Process Rewards and Shaped RewardsMedium

  Implement the core of Group Relative Policy Optimization in a NumPy notebook: group-normalized advantages, the clipped objective with a KL penalty, per-token credit from process rewards, and shaped rewards. Tests fluency with array axes and broadcasting and an understanding of how reward normalization interacts with shaping terms.

  AmazonMachine Learning EngineerMachine LearningOnsite
• Implement Batch Gradient Descent for Linear RegressionEasy

  Amazon data scientist machine learning prompt on writing batch gradient descent pseudocode for linear regression, including MSE loss, vectorized gradients, parameter updates, stopping criteria, and implementation checks.

  AmazonData ScientistMachine LearningTechnical Screen
• Implement Multi-Head Attention with a KV Cache and Cached Grouped-Query AttentionMedium

  Implement causal multi-head attention from scratch, add a key-value cache for token-by-token decoding, and extend the forward pass to grouped-query attention, where several query heads share one key and value head. Tests tensor shape discipline, masking with cached positions, and reasoning about decode compute and cache memory.

  AmazonMachine Learning EngineerMachine LearningOnsite
• Implement SGD for linear regression and derive gradientsMedium

  This question evaluates understanding of gradient-based optimization and linear model training, including competency in deriving gradients for mean squared error and implementing stochastic (or mini-batch) gradient descent for parameter estimation.

  AmazonApplied ScientistMachine LearningTechnical Screen
• LLM Fundamentals: Tokenization Design and KL-Regularized SFTMedium

  This problem tests your understanding of subword tokenization strategies and the theoretical motivation behind KL-divergence constraints during supervised fine-tuning.

  AmazonApplied ScientistMachine LearningOnsite
• List hyperparameter tuning methodsEasy

  This question evaluates understanding of hyperparameter tuning methods, including the competencies to compare different search strategies, analyze trade-offs in efficiency and parallelization, and design experiments to optimize model performance.

  AmazonApplied ScientistMachine LearningTechnical Screen
• Optimize Email Strategy for New Prime Video Series LaunchMedium

  Evaluates email targeting and ranking strategy for a new Prime Video series launch. Strong answers cover data needs, cold start, ranking models, offline metrics, A/B testing, guardrails, and model selection.

  AmazonData ScientistMachine LearningOnsite
• Optimize Predictive Analytics: Feature Engineering to Model EvaluationMedium

  Evaluates end-to-end predictive analytics project communication for a technical data-science interview. Strong answers cover business objectives, data, leakage controls, features, models, evaluation, errors, and impact.

  AmazonData ScientistMachine LearningTechnical Screen
• Optimize XGBoost for Predicting Marketing OutcomesMedium

  Optimize XGBoost for Predicting Marketing Outcomes evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonData ScientistMachine LearningOnsite
• Test whether two user populations differMedium

  This question evaluates competency in statistical inference and multivariate analysis, covering hypothesis formulation about equal distributions, preprocessing and normalization considerations, choice of statistical tests or modeling approaches, and interpretation of significance and effect size.

  AmazonApplied ScientistMachine LearningOnsite
• Use a Fitted Line to Predict a Future Data PointHard

  This question tests the ability to implement a simple linear regression predictor and analyze the reliability of extrapolating time-series data using basic statistical heuristics.

  AmazonSoftware EngineerMachine LearningTechnical Screen