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Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
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- 546
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- 11
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Showing 301–330 of 4616 results
- AmazonMediumData ScientistExplain Propensity Score Matching and Assess Covariate Balance
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Explain Propensity Score Matching and Assess Covariate Balance states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - AmazonMediumData ScientistExplain Statistical Outputs to Non-Technical Stakeholders
Explain Statistical Outputs to Non-Technical Stakeholders evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - AmazonMediumMachine Learning EngineerExplain Technical Contributions and Delivery Under a Tight Deadline
Explain a research or technical contribution and a deadline-driven delivery decision, with evidence, clear ownership, and honest limitations.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerExplain Your Interest in an Organization and Role
Explain your interest in an organization and engineering role using specific reasons, relevant project experience, and a realistic contribution.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerExplain a Project That Could Not Meet a Tight Deadline
Explain a missed or renegotiated deadline with early risk signals, personal accountability, explicit scope trade-offs, and a concrete planning improvement.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerExplain an AI-Agent Project and Its Adoption
Tests your ability to communicate end-to-end technical leadership for an AI agent system, covering technical architecture, stakeholder collaboration, and concrete user-adoption metrics.
Behavioral & LeadershipTechnical ScreenBehavioralAI AgentsSystem Design - AmazonMediumApplied ScientistExplain core ML concepts and diagnostics
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.
Machine LearningTechnical Screen - AmazonMediumSoftware EngineerExplain deadlines, going beyond, and persuasion
Explain deadlines, going beyond, and persuasion evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - AmazonHardApplied ScientistExplain imbalance, metrics, bias-variance, Transformers vs. CNNs
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.
Machine LearningTechnical Screen - AmazonMediumSoftware EngineerExplain overfitting, regularization, and LLM techniques
This question tests the conceptual understanding of model generalization trade-offs, the mathematical mechanics of regularization, and the practical application of modern LLM architectures.
Machine LearningTechnical ScreenRegularizationLLMRAG - AmazonMediumApplied ScientistExplain surprisal and its units
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.
Machine LearningTechnical Screen - AmazonMediumData ScientistExplain why LASSO selects features
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.
Machine LearningTechnical Screen - AmazonMediumSoftware EngineerExplain work-simulation approach under ambiguity
This question evaluates a software engineer's ability to prioritize tasks, manage multiple stakeholders' requests, document assumptions and decisions, and deliver incremental value within a time-boxed, ambiguous assignment.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistExplore Subscription Patterns and Status Transitions with SQL/Pandas
This question evaluates proficiency in time-series and event-sequence data manipulation, temporal aggregation, and data quality validation using SQL and pandas, focusing on identifying status transitions and date-based uniqueness.
Data Manipulation (SQL/Python)Technical Screen - AmazonHardSoftware EngineerFind Two-Word Compound Words
This problem tests string manipulation, hash set lookups, and the ability to efficiently identify compound words formed by the concatenation of exactly two distinct words from a provided collection.
Coding & AlgorithmsTechnical ScreenStringsHash SetAlgorithms - AmazonEasySoftware EngineerFind all cells reachable by downhill flow
This question evaluates graph traversal and reachability reasoning on a 2D elevation grid, testing algorithmic problem-solving, complexity analysis, and handling of monotonic flow constraints.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerFind concatenated words in list
This question evaluates proficiency in string manipulation, efficient lookup structures (e.g., hashing/trie) and dynamic programming or recursion for word segmentation, testing algorithmic efficiency and data-structure selection within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerFind first unique character
Find first unique character evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerFind max equal-frequency block count for each prefix
This problem tests your ability to efficiently manage character frequency tracking while processing string prefixes, using hash maps or frequency arrays to validate partitioning conditions.
Coding & AlgorithmsTechnical ScreenStringsDynamic ProgrammingHash Map - AmazonMediumData ScientistFind recommended friend pairs by shared songs
This question evaluates data manipulation skills—specifically SQL joins and anti-joins, deduplication, date/time handling, aggregation, and pairwise overlap computation—within the Data Manipulation (SQL/Python) domain at a practical implementation level requiring query-writing to produce per-day overlap counts.
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumSoftware EngineerFind shortest subarray to restore order
This question evaluates array manipulation and algorithmic problem-solving skills, focusing on understanding how local subarray sorting affects global order, edge-case reasoning (already-sorted arrays, duplicates, strictly decreasing sequences), and complexity analysis within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - AmazonMediumApplied ScientistFind shortest transformation steps in a word graph
This question evaluates a candidate's ability to model string transformations as a graph, apply graph traversal and shortest-path reasoning, and manage efficient string-based search and data-structure choices within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - AmazonEasySoftware EngineerFind the Earliest Pair with a Target Sum
Tests array traversal and hash table lookups to find a two-sum pair that minimizes the second index, running in linear time and space.
Coding & AlgorithmsTechnical Screenarrayhash-tabletwo-sum - AmazonMediumSoftware EngineerFind the K-th Smallest Value in Two Sorted Arrays
Given two nondecreasing integer arrays and a one-based integer k, return the k-th smallest value in their combined multiset. Work through the function contract, boundary cases, correctness argument, and time and space complexity expected in a production-quality solution.
Coding & AlgorithmsTechnical Screen - AmazonEasySoftware EngineerFind the Lowest Common Ancestor in a Binary Tree
This problem tests tree traversal techniques and recursive logic to identify the deepest node that is an ancestor to two specified nodes within a binary tree.
Coding & AlgorithmsTechnical ScreenBinary TreeRecursionDFS - AmazonEasySoftware EngineerFind the Most Frequently Used Meeting Room
Simulate meeting room assignments with delayed starts and duration preservation using priority queues to find the most utilized room.
Coding & AlgorithmsTechnical ScreenHeapSimulationGreedy - AmazonEasySoftware EngineerFind the Single Value in a Sorted Array of Pairs
This problem tests the ability to optimize a linear search into a logarithmic one by leveraging the properties of sorted pairs using binary search.
Coding & AlgorithmsTechnical ScreenBinary SearchArraysDivide and Conquer - AmazonMediumSoftware EngineerFind top-k lottery winners by spending
This problem tests the ability to manage top-k selection from a stream or static collection using heap data structures while enforcing deterministic tie-breaking rules.
Coding & AlgorithmsTechnical ScreenHeapsSortingAlgorithms - AmazonMediumApplied ScientistFind two numbers that sum to target
This question evaluates a candidate's competency in basic algorithmic problem-solving within the Coding & Algorithms domain, focusing on array manipulation, pair-sum reasoning, and analysis of time-space trade-offs.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerFind when failures started in log stream
This problem tests the ability to identify the first occurrence of a specific condition in a sorted boolean stream using a modified binary search approach.
Coding & AlgorithmsTechnical ScreenBinary SearchAlgorithmsOptimization
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