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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
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 331–360 of 9403 results
- AmazonMediumSoftware EngineerBuild the Largest Available Sequence
This problem tests your ability to use a greedy approach combined with a frequency map to construct the lexicographically largest sequence under specific constraints.
Coding & AlgorithmsOnline AssessmentGreedyPriority QueueFrequency Map - AmazonMediumSoftware EngineerCaesar Cipher with Translation-Table Optimization
This question tests string manipulation, modular arithmetic, and the ability to optimize performance by trading space for time using a precomputed lookup table.
Coding & AlgorithmsTechnical ScreenStringsOptimizationAlgorithms - AmazonMediumData ScientistCalculate 7-Day Rolling Average for Energy Consumption
This question evaluates programming fluency in numerical data processing and algorithmic implementation for computing moving averages and handling edge cases in time-series sequences, and it applies to Data Scientist roles while falling under the Coding & Algorithms category.
Coding & AlgorithmsOnsite - AmazonMediumData ScientistCalculate A/B sample size, CI, decision rules
This question evaluates skills in experimental design and statistical inference, including sample size calculation, hypothesis testing, confidence interval interpretation, multiple comparison control, variance reduction techniques, and operational considerations for A/B testing.
Statistics & MathOnsite - AmazonMediumSoftware EngineerCalculate Circular Route Query Distance
This question evaluates a candidate's ability to reason about circular arrays, distance computations, and query-efficient algorithms, assessing concepts such as modular indexing and cumulative distances.
Coding & AlgorithmsTechnical Screen - AmazonMediumData ScientistCalculate Defect Rate and Identify Top Lanes for Carriers
This question evaluates competency in SQL-based data manipulation for a data scientist role, covering joins, aggregations, date-based filtering, metric computation, and use of window functions to derive week-over-week changes.
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumData ScientistCalculate Monthly Revenue from Orders in 2023
This question evaluates proficiency in SQL-based data manipulation, including aggregation, date extraction, and combining transactional tables to produce business metrics, and falls under the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumBusiness Intelligence EngineerCalculate Rolling 7-Day Sum of Answers by Device
This question evaluates proficiency in time-series aggregation and group-wise metric computation on structured data, specifically calculating rolling seven-day sums of answers per device, and assesses familiarity with data manipulation using SQL or Python.
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistCalculate Weekly, Monthly Hours Watched by Premium Users
This question evaluates a candidate's ability to perform time-based aggregation and data manipulation in SQL and Python, including grouping, filtering by subscription status, and converting minute-level metrics into hours.
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumSoftware EngineerCalculated Risks and Tight Deadlines: Moving Fast With Good Judgment
An Amazon leadership-principles behavioral question in two parts: a calculated risk you took when speed was critical, and an important project you delivered under a tight deadline. It tests bias for action, risk judgment, scope negotiation and delivering results at a senior level.
Behavioral & LeadershipOnsite - AmazonEasySoftware EngineerCheck a Palindrome While Ignoring Case and Punctuation
Tests string manipulation and two-pointer traversal techniques to verify if a string is a palindrome while ignoring non-alphanumeric characters and case.
Coding & AlgorithmsTechnical ScreenStringsTwo PointersEasy - AmazonMediumApplied ScientistCheck if adding edge creates cycle in digraph
This question evaluates understanding of graph algorithms, reachability, and algorithmic complexity by determining whether adding a directed edge would introduce a cycle in an otherwise acyclic directed graph.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerCheck path in N-ary tree
This question evaluates understanding of tree traversal and sequence matching in N-ary trees, including handling non-unique node values and maintaining traversal state across branches.
Coding & AlgorithmsOnsite - AmazonHardData ScientistChoose Between Fine-Tuning and RAG for Client Chatbot
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 Choose Between Fine-Tuning and RAG for Client Chatbot states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - AmazonMediumData ScientistChoose Effective Graphs for Data Exploration
Evaluates chart selection for exploratory data analysis across categorical and numerical variables. Strong answers explain pie charts, distributions, comparisons, relationships, missingness, and when visuals mislead.
Analytics & ExperimentationOnsite - AmazonHardData ScientistChoose Models for Imbalanced Data and Time-Series Forecasting
Choose Models for Imbalanced Data and Time-Series Forecasting 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.
Machine LearningTechnical Screen - AmazonMediumSoftware EngineerChoose Work-Style Assessment Responses
Tests a candidate's ability to navigate forced-choice and situational judgment items in an automated work-style assessment, balancing trade-offs between competing leadership values such as speed versus quality, autonomy versus consensus, and maintaining internal consistency.
Behavioral & LeadershipOnline AssessmentAmazon OAWork Style AssessmentBehavioral - AmazonMediumData ScientistChoose regularization norms and model formulations
This question evaluates understanding of regularization norms (L0, L1, L2, L∞), objective formulations for linear and logistic regression, and practical competencies in handling collinearity, outliers, feature scaling, and optimization difficulty.
Machine LearningTechnical Screen - AmazonHardSoftware EngineerCircular Drone Hub Delivery Route
This question evaluates a candidate's ability to work with circular array structures and prefix sums to compute shortest distances on a ring topology. It tests practical coding skill in efficiently handling wraparound arithmetic and cumulative sums, a common pattern in algorithm interviews assessing array manipulation and optimization under scale constraints.
Coding & AlgorithmsOnline Assessment - AmazonEasySoftware EngineerClarify internship availability and conversion
This question evaluates a candidate's clarity about availability, logistical constraints, and intent to convert an internship into a full-time role, testing communication of exact dates, work authorization, relocation, and academic conflicts.
Behavioral & LeadershipTechnical Screen - AmazonMediumProduct ManagerCommunicating Top-Down Change
Practice communicating top-down operational change across peer managers, direct-report managers, and front-line employees. The solution covers safety, efficiency, cost goals, audience-specific messaging, pilots, feedback loops, metrics, guardrails, and sustainment.
Behavioral & LeadershipOnline Assessment - AmazonMediumData ScientistCompare RNNs and Transformers for Long-Sequence Text Classification
Compare RNNs and Transformers for Long-Sequence Text Classification 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.
Machine LearningTechnical Screen - AmazonHardData ScientistCompare Random Forests vs Gradient Boosting rigorously
This question evaluates a candidate's ability to choose and configure tree-based models (Random Forest vs. Gradient Boosted Trees), handle high-cardinality categorical features and missingness, mitigate class imbalance and label noise, produce reliable feature importance and calibration, and design an experiment and inference strategy that meets strict latency and resource constraints. It is commonly asked in the Machine Learning domain for Data Scientist roles to assess trade-offs in bias–variance, robustness to correlated features and noise, hyperparameter and encoding decisions, experiment design and evaluation, and tests both conceptual understanding and practical application for production-ready systems.
Machine LearningTechnical Screen - AmazonMediumData ScientistCompare Regularization Techniques and Their Use Cases
Evaluates model evaluation, regularization, and regression basics for predictive analytics. Strong answers define precision and recall, compare L1, L2, L0, and L-infinity penalties, state OLS assumptions, and contrast linear and logistic regression formulas and losses.
Machine LearningTechnical Screen - AmazonHardData ScientistCompare Tableau live vs extract and filters
This question evaluates competency in BI data architecture and large-scale dashboard performance, covering extract vs live connections, refresh and incremental strategies, query pushdown versus in-tool processing, row-level security, filter order-of-operations, performance tuning, and platform migration mapping within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to assess trade-off reasoning across data freshness, storage, query performance and security for a 100M-row fact table, combining practical application-level decisions (implementation and tuning) with conceptual understanding (filter ordering, calculations, and migration pitfalls) when moving between BI platforms.
Analytics & ExperimentationOnsite - AmazonMediumSoftware EngineerCompute Edit Distance
Tests dynamic programming and string manipulation by calculating the minimum number of single-character edits (insertions, deletions, substitutions) needed to transform one string into another.
Coding & AlgorithmsTechnical ScreenDynamic ProgrammingStringsAlgorithms - AmazonMediumBusiness Intelligence EngineerCompute First Order Proportions by Day and Category
This question evaluates a candidate's ability to perform event-level data manipulation and customer-level deduplication using SQL or Python, focusing on calculating the proportion of orders that represent a customer's first purchase by day and category.
Data Manipulation (SQL/Python)Onsite - AmazonMediumSoftware EngineerCompute Latencies and Search Grid Path
This question evaluates parsing and processing of timestamped event logs to compute message latencies and implementation of constrained grid path search, testing competencies in file I/O, record alignment/matching, time-delta computation, graph traversal, and stateful backtracking.
Coding & AlgorithmsTechnical Screen - AmazonMediumData ScientistCompute a Rolling Seven-Day Revenue Sum with Missing Dates
Tests the ability to write a PostgreSQL query that generates a continuous daily time series per seller, handles missing sales dates, filters by order status, and computes a rolling 7-day revenue sum.
Data Manipulation (SQL/Python)Technical Screensqlpostgresqlwindow-functions - AmazonMediumData ScientistCompute an A/B test p-value by hand
This question evaluates understanding of hypothesis testing for proportions, specifically the two-proportion z-test, p-value computation and interpretation of statistical significance in an A/B test with binary outcomes.
Statistics & MathTechnical Screen
Amazon