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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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- 546
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Showing 8611–8640 of 9403 results
- Two SigmaMediumQuantitative ResearcherLinear Interpolator with Extrapolation
This question evaluates understanding of piecewise linear interpolation and extrapolation, numerical robustness with floating-point values, and algorithmic competencies such as sorting and efficient query location for large datasets.
Coding & AlgorithmsTechnical Screen - Two SigmaMediumSoftware EngineerMatch Limit Orders with Price-Time Priority
Implement a limit order matching engine adhering to price-time priority (FIFO), resting-order execution pricing, and partial fill mechanics.
Coding & AlgorithmsTechnical ScreenHeapData StructuresSimulation - Two SigmaEasyData ScientistMatch readings with latest same-city humidity
This question evaluates the ability to align and join time-ordered sensor data by key, testing skills in time-series merging, per-key state management, and handling absent matches.
Coding & AlgorithmsTechnical Screen - Two SigmaMediumSoftware EngineerMaximize Capital by Completing at Most k Projects
This question evaluates algorithmic problem-solving and optimization skills within the Coding & Algorithms domain, focusing on selection under resource and operation constraints and reasoning about time and space complexity.
Coding & AlgorithmsTechnical Screen - Two SigmaHardData ScientistMaximum-Product Simple Path in a Complete Directed Graph
This question evaluates skills in graph algorithms, multiplicative path optimization, and combinatorial dynamic programming, testing competency in reasoning about simple paths and product-based scoring in directed weighted graphs.
Coding & AlgorithmsOnsite - Two SigmaHardData ScientistMerge Two Sorted Lists
This problem tests your ability to manipulate linked list pointers to merge two sorted sequences efficiently without allocating extra space for new nodes.
Coding & AlgorithmsTechnical ScreenLinked ListsPointersRecursion - Two SigmaHardData ScientistMerge two sorted linked lists
This problem tests your ability to manipulate linked list pointers to merge two sorted structures in-place, focusing on space optimization and handling edge cases.
Coding & AlgorithmsTechnical ScreenLinked ListsPointersIn-place - Two SigmaMediumData ScientistPerform no-intercept linear regression from two datasets
This question evaluates linear regression modeling, numerical linear algebra (normal equations and stable factorization methods), regularization, R² interpretation, and data-preprocessing/validation skills within the Machine Learning domain for a Data Scientist role.
Machine LearningOnline Assessment - Two SigmaHardData ScientistPredict Citi Bike Demand at a Specific NYC Station
This question evaluates time-series forecasting, demand estimation, and ML system-design competencies, focusing on problem framing, handling censored observations, data integration, model selection, and evaluation under operational constraints.
ML System DesignTechnical Screen - Two SigmaMediumData ScientistPredict Stock Prices from Google Search Data
This question evaluates a candidate's competency in time-series and cross-sectional predictive modeling, alternative-data feature engineering, and rigorous validation to prevent temporal leakage within the Machine Learning domain.
Machine LearningTechnical Screen - Two SigmaHardData ScientistPredict a Company's Stock Price from Six Years of Web Search Logs
This question evaluates proficiency in feature engineering and time-series forecasting from large-scale event logs, including entity linking, aggregation and normalization, handling non-stationarity and drift, and model selection and validation for predicting a given company's stock price.
Machine LearningTechnical Screen - Two SigmaHardData ScientistPredict bike demand and avoid overfitting
This question evaluates time-series forecasting, feature engineering, awareness of data leakage risks, model evaluation choices, and overfitting prevention competencies within the Machine Learning domain applied to demand prediction.
Machine LearningTechnical Screen - Two SigmaHardData ScientistPredicting Stock Prices from Twitter Data
This question evaluates competency in ML system design for integrating NLP-derived signals from social media with time-series financial modeling, including feature extraction, timestamp alignment, and handling low signal-to-noise ratios.
ML System DesignTechnical Screen - Two SigmaMediumData ScientistReturn sorted values after quadratic transform
This problem tests your ability to efficiently transform elements in a sorted array using a quadratic function and merge them back into a single sorted array in linear time.
Coding & AlgorithmsTechnical ScreenArraysTwo PointersSorting - Two SigmaHardData ScientistSlopes of y-on-x vs x-on-y Regression: Relationship and When They Are Equal
This question evaluates understanding of simple OLS linear regression estimators, moment-based expressions for slopes, the relationship between y-on-x and x-on-y fitted slopes, and how correlation and variable scaling influence coefficient values.
Machine LearningTechnical Screen - Two SigmaMediumSoftware EngineerSolve capital selection and grid escape
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Solve capital selection and grid escape states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - Two SigmaMediumMachine Learning EngineerSolve tree partition and IPO allocation
This question evaluates algorithmic problem-solving skills in tree processing and allocation simulations, specifically assessing understanding of subtree-sum computation for tree partitioning and deterministic round-robin allocation logic for IPO-style share distribution, along with appropriate data structure selection and correctness under edge cases. It is commonly asked in the Coding & Algorithms domain because it measures practical application-level implementation and the ability to analyze time and space complexity rather than purely conceptual proof.
Coding & AlgorithmsOnline Assessment - Two SigmaMediumData ScientistWhy Is the Sample Mean Approximately Normal for Large Samples?
This question evaluates understanding of the Central Limit Theorem and asymptotic statistical reasoning, including recognition that the standardized sample mean (equivalently the standardized sum) becomes approximately normal and how that fact underpins inference such as confidence intervals.
Machine LearningTechnical Screen - Two SigmaHardData ScientistWhy Use a t-Statistic?
This problem tests your conceptual understanding of the t-statistic, specifically how sample parameters influence its value and how it bridges the gap between estimation and hypothesis testing.
Statistics & MathTechnical ScreenStatisticsHypothesis TestingQuantitative Finance - Two SigmaEasyData ScientistWhy is the t-statistic useful
This question tests the candidate's conceptual understanding of the t-statistic, specifically its role in hypothesis testing when population parameters are unknown.
Statistics & MathTechnical ScreenStatisticsHypothesis TestingInference - Two SigmaHardData ScientistWhy use the t-statistic?
This question tests the conceptual understanding of the t-statistic, specifically why it is used instead of the Z-statistic when population parameters are unknown and how it accounts for uncertainty in sample variance.
Statistics & MathTechnical ScreenStatisticsHypothesis TestingT-Distribution - UberMediumSoftware EngineerAligning Two Teams with Conflicting Priorities and Learning from a Mistake
Two senior behavioral prompts: describe bringing two teams with conflicting priorities into alignment to deliver an important project, and a mistake you made, the feedback you received and how you closed the gap. It assesses cross-team influence, conflict resolution and growth.
Behavioral & LeadershipOnsite - UberMediumData ScientistAnalyze Cancellation Change with Statistics
This question evaluates statistical inference for binary outcomes, regression adjustment for confounding, experiment design and power analysis, and nonparametric uncertainty estimation using bootstrapping, and is targeted at Data Scientist roles within the Statistics & Math domain.
Statistics & MathTechnical Screen - UberEasyMachine Learning EngineerAnalyze sales with groupby
This question evaluates proficiency in data manipulation and analytics using pandas, specifically the ability to join relational tables and apply groupby aggregations to compute business metrics like total sales, average sales per order, and completion rates.
Coding & AlgorithmsTechnical Screen - UberMediumSoftware EngineerAnswer Common Behavioral Questions
This question assesses the candidate's ability to navigate professional conflict and technical disagreements while maintaining constructive relationships and project momentum.
Behavioral & LeadershipOnsiteBehavioralConflict ResolutionLeadership - UberHardSoftware EngineerAnswer Repeated Shortest Increasing-Path Queries
Preprocess shortest strictly increasing grid paths for repeated target queries, with deterministic BFS ties, predecessor reconstruction, and memory trade-offs.
Coding & AlgorithmsOnsite - UberMediumMachine Learning EngineerAnswer leadership questions on tradeoffs and collaboration
This set of behavioral leadership questions evaluates leadership competencies including trade-off decision-making, mentorship, cross-functional collaboration, roadmap prioritization, and conflict resolution for a Machine Learning Engineer within the Behavioral & Leadership domain, focusing on practical application through recounting real project experiences. These prompts are commonly asked to probe both conceptual understanding of trade-offs and organizational dynamics and the practical application of influence, communication, and judgment when aligning stakeholders and prioritizing work.
Behavioral & LeadershipOnsite - UberHardData ScientistApply instrumental variables under interference
This question evaluates understanding of causal inference with instrumental variables in the presence of interference, testing skills in defining units and aggregation levels for market‑level spillovers, articulating IV assumptions (relevance, exclusion restriction, independence, monotonicity), and formulating estimation frameworks such as two‑stage least squares. Commonly asked in Statistics & Math interviews for data scientist roles because networked marketplaces invalidate simple A/B tests, it sits in the econometrics/causal inference domain and primarily assesses practical application of IV methods while requiring conceptual understanding of identification, robustness diagnostics, and sensitivity analysis.
Statistics & MathTechnical Screen - UberMediumSoftware EngineerAssign Meetings to the Minimum Number of Rooms
Assign meetings to the minimum number of rooms using deterministic room ID allocation, returning the room assigned to each meeting in original input order.
Coding & AlgorithmsOnsiteheapintervalssorting - UberMediumSoftware EngineerBehavioral: your most complex project, a conflict you resolved, and a failure
Three hiring-manager behavioral questions: describe your most complex project, a conflict you resolved, and a failure you owned. Answers are judged on STAR structure, personal ownership, concrete and measurable results, a fair account of other people's views, and lessons that were actually applied later.
Behavioral & LeadershipOnsite
Two Sigma
Uber