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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 121–150 of 3640 results
- AmazonMediumProduct ManagerBehavioral Problem-Solving Scenarios
Practice Amazon Product Manager behavioral scenarios with structured STAR examples for data-driven problem solving, complex constraints, difficult customers, fast decisions, and ownership beyond scope. The solution emphasizes metrics, trade-offs, guardrails, reusable mechanisms, and interview-ready answer patterns.
Behavioral & LeadershipOnsite - AmazonMediumSoftware EngineerBehavioral: Resolving a Workplace Conflict and Using Generative AI in Your Work
A two-part behavioral question from a software engineering onsite: describe a conflict with a coworker and how it was resolved, then explain how you have used generative AI in your work. It tests ownership, settling disagreements with evidence, verifying AI output, and judgment about the limits of AI tools.
Behavioral & LeadershipOnsite - AmazonMediumFrontend EngineerBuild Accessible Data-Loading Popovers for Multiple Buttons
This problem tests the ability to build a robust, accessible UI component that manages asynchronous data fetching, state synchronization, and DOM-level focus management.
Software Engineering FundamentalsOnsiteFrontendReactAccessibility - AmazonHardSoftware EngineerBuild a Searchable Sentence Index
This problem tests your ability to design an in-memory inverted index for text retrieval, requiring efficient tokenization, case-insensitive mapping, and dynamic updates to a collection of sentences.
Coding & AlgorithmsOnsiteHash MapsInverted IndexData Structures - AmazonMediumSoftware EngineerBuild a Solution That Benefits Teams Beyond Your Own
This question assesses a candidate's ability to think beyond immediate team requirements, identifying systemic inefficiencies and designing scalable solutions that provide cross-functional value.
Behavioral & LeadershipOnsiteLeadershipCross-functionalScalability - AmazonHardData ScientistBuild a package-allocation model for couriers
This question evaluates a candidate's ability to design an end-to-end ML-driven package-to-courier allocation system, testing competencies in predictive modeling (per-stop service-time forecasting), constrained combinatorial optimization for routing and fairness, feature engineering, simulation, and production concerns such as cold-start handling and distribution shift. It is asked to assess how candidates balance competing objectives like on-time delivery versus fairness under real-world constraints (shift hours, vehicle capacity, delivery windows, time-varying travel forecasts), and is categorized under Machine Learning with a focus on practical application-level system design rather than purely conceptual theory.
Machine LearningOnsite - AmazonHardApplied ScientistBuild an end-to-end ML pipeline
Build an end-to-end ML pipeline evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite - 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 - 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 - 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 - 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 - 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 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 - 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 - AmazonMediumApplied ScientistCompute and Explain Mean and Variance
This question tests your understanding of statistical estimators for mean and variance, the derivation of Bessel's correction, catastrophic cancellation in naive computations, and numerically stable single-pass algorithms like Welford's.
Statistics & MathOnsiteStatisticsNumerical AnalysisAlgorithms - AmazonMediumData ScientistCompute daily work hours from in/out events
This question evaluates a candidate's ability to manipulate time-series punch-event data, including temporal pairing of in/out events, splitting shifts across calendar days, rounding and aggregating hours, and detecting data-quality issues such as overlaps or orphaned events using SQL or Python.
Data Manipulation (SQL/Python)Onsite - AmazonMediumSoftware EngineerCompute fruit spoilage time in a grid
This question evaluates a candidate's understanding of graph traversal concepts, grid-based state propagation, and reachability analysis in time-evolving systems.
Coding & AlgorithmsOnsite - AmazonMediumData ScientistCompute join counts and window ranks
This question evaluates SQL data-manipulation and analytic competencies including join semantics, set operations, window functions, view versus materialized view trade-offs, key and partition design, and query-tuning techniques in the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Onsite - AmazonMediumSoftware EngineerCompute optimal locker placement with obstacles
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compute optimal locker placement with obstacles states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - AmazonMediumSoftware EngineerCompute product excluding index without division
This question evaluates array-manipulation and algorithmic problem-solving skills, focusing on computing a product-for-each-index using prefix and postfix accumulation concepts while reasoning about edge cases such as zeros, negative numbers, and integer overflow.
Coding & AlgorithmsOnsite - AmazonMediumSoftware EngineerCompute the Median After Each Stream Insertion
This problem tests your ability to maintain dynamic statistics in a data stream using efficient data structures to achieve logarithmic insertion and constant-time retrieval.
Coding & AlgorithmsOnsiteHeapsData StreamsSorting - AmazonMediumBusiness Intelligence EngineerCount Customers Buying Both 'Kindle' and 'Alexa'
This question evaluates proficiency in relational data manipulation and set-based reasoning, assessing SQL competency such as joins, aggregation, and identifying distinct customers who purchased multiple specified products.
Data Manipulation (SQL/Python)Onsite - AmazonMediumSoftware EngineerCount expressions reaching a target sum
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Count expressions reaching a target sum states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - AmazonMediumSoftware EngineerCustomer Focus: Going Above and Beyond and Shipping a Customer-Driven Change
An Amazon behavioral question on customer focus: describe a time you went above and beyond for a customer, and a change you made that was driven by customer needs. It tests how you discover customer problems, act on them beyond expectations, and measure the impact.
Behavioral & LeadershipOnsite - AmazonHardApplied ScientistDeep-dive your GenAI project architecture
Deep-dive your GenAI project architecture evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite - AmazonMediumSoftware EngineerDeliver an Important Project Under a Tight Deadline
Describe deadline-driven project delivery through business priorities, scope and quality tradeoffs, dependency management, and evidence of the delivered impact.
Behavioral & LeadershipOnsite - AmazonMediumData ScientistDemonstrate Amazon LP with deep follow-ups
This question evaluates behavioral and leadership competencies for a Data Scientist role, focusing on customer orientation, ownership, accountability for missed deadlines, and responsiveness to negative feedback while requiring quantified impact, scope, dates, and trade-offs.
Behavioral & LeadershipOnsite - AmazonMediumData ScientistDemonstrate Leadership and Ownership in Energy Analytics Role
Amazon-style data scientist behavioral prompt for an energy analytics role, covering Ownership, STAR storytelling, resume walkthrough, transferable analytics skills, quantified impact, and motivation for the domain.
Behavioral & LeadershipOnsite - AmazonHardData ScientistDemonstrate Leadership in Challenging Situations and Decision-Making
Demonstrate Leadership in Challenging Situations and Decision-Making 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 & LeadershipOnsite
Amazon