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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.
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Showing 631–660 of 9403 results
- 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 - AmazonMediumSoftware EngineerExplain how you use AI tools to learn and code, and defend a past AI project
A hiring-manager discussion for an entry-level software engineer about working with AI tools. It asks how the candidate uses AI to learn and to code, then probes a past AI project: whether AI was truly needed, how its output is verified, and how it would scale in a very large organization.
Behavioral & LeadershipOnsite - 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 - AmazonHardApplied ScientistExplain key ML theory and techniques
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.
Machine LearningOnsite - 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 parallelism and collectives in training
This question evaluates a candidate's competency in designing scalable distributed training systems, covering parallelism strategies (data, model/tensor, pipeline), communication collectives (all-reduce, all-gather, reduce-scatter, broadcast), and tensor-level layer partitioning such as column- and row-parallel splits.
ML System DesignOnsite - 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 - AmazonHardSoftware EngineerExplain the Most Complex Problem You Solved
This question tests your ability to structure a high-stakes technical narrative using the STAR (Situation, Task, Action, Result) method while demonstrating depth of engineering judgment.
Behavioral & LeadershipHR ScreenBehavioralLeadershipSystem Design - AmazonMediumApplied ScientistExplain why CTR rises but CVR unchanged
This question tests the ability to diagnose Simpson's Paradox or composition shifts in funnel metrics when engagement increases without a corresponding lift in final conversion.
Analytics & ExperimentationOnsiteAnalyticsExperimentationFunnel Analysis - 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 Dataset to Assess Quality and Choose Visualizations
Evaluates fast dataset exploration, data-quality assessment, and visualization planning for business stakeholders. Strong answers profile schema, check missingness and bias, and choose charts aligned to decisions.
Analytics & ExperimentationOnsite - 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 EngineerFewest Severity Inversions When Interleaving Two Patch Queues
Merge two severity-labeled patch queues into one install order that keeps each queue's internal order, and return the minimum number of severity inversions over every valid merge. Tests DP over two sequences and efficient counting with letters as priorities.
Coding & AlgorithmsOnline Assessmentdynamic-programmingstringsinversions - AmazonHardSoftware EngineerFind Longest Activatable Server Streak
This problem tests your ability to use the sliding window technique to find the longest contiguous subarray that satisfies a specific count-based constraint.
Coding & AlgorithmsOnline AssessmentSliding WindowTwo PointersArray - AmazonMediumBusiness Intelligence EngineerFind Top Three Books by City in Recent Months
This question evaluates proficiency in time-windowed aggregation, group-level ranking and window-function usage on relational datasets, emphasizing handling of recent date ranges and top-N selection.
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistFind Top-3 Salaries Per Department Using SQL
This question evaluates SQL data manipulation competency, focusing on group-wise aggregation, ordering, and selecting the top-N salary values within each department.
Data Manipulation (SQL/Python)Onsite - 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 - AmazonHardSoftware EngineerFind a Maximum-Sum Window in a Sparse Array
This problem tests the ability to optimize sliding window calculations for extremely large arrays by leveraging compressed data structures (run-length encoding) rather than materializing the full array.
Coding & AlgorithmsOnline AssessmentSliding WindowArraysOptimization - AmazonHardSoftware EngineerFind a Valid Course Order
This problem tests your ability to detect cycles in a directed graph and perform a topological sort to resolve dependencies.
Coding & AlgorithmsOnsiteGraphsTopological SortKahn's Algorithm - AmazonMediumSoftware EngineerFind a valid dependency order
This question evaluates understanding of directed graphs, dependency resolution and cycle detection, and the ability to reason about valid ordering constraints within a graph representation.
Coding & AlgorithmsOnline Assessment - 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 longest common contiguous subarray
This question evaluates a candidate's proficiency in array and substring algorithm design, including concepts such as dynamic programming, hashing, and efficient search techniques.
Coding & AlgorithmsOnsite - AmazonMediumSoftware EngineerFind longest segment with dominant ends
This question evaluates algorithmic problem-solving with arrays and complexity-aware design, testing skills in efficient subarray identification, boundary reasoning, and performance analysis within the Coding & Algorithms domain.
Coding & AlgorithmsOnline Assessment - AmazonMediumSoftware EngineerFind longest segment with endpoints > interior
This question evaluates algorithmic design and array-processing skills, specifically reasoning about subarray endpoint vs interior relationships, range comparisons, and designing solutions with subquadratic time and space complexity.
Coding & AlgorithmsOnline Assessment - 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 - AmazonMediumSoftware EngineerFind minimum days to deliver all orders
This question evaluates algorithm design and combinatorial optimization skills, focusing on scheduling and load-balancing under per-center capacity constraints and frequency-based counting.
Coding & AlgorithmsOnline Assessment
Amazon