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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 2221–2250 of 4616 results
- LinkedInMediumData ScientistResolve Conflicting A/B Test Results in Cities
Evaluates interpretation of conflicting A/B test results across cities using Simpson's paradox and stratified estimands. Strong answers check weighting, SRM, heterogeneity, confidence intervals, and rollout criteria.
Analytics & ExperimentationTechnical Screen - LinkedInEasyData ScientistResolve Simpson’s paradox in A/B email test
This question tests your ability to identify and resolve Simpson's Paradox in A/B test results, where an aggregate trend disappears or reverses when segmented by user cohorts.
Analytics & ExperimentationTechnical ScreenA/B TestingSimpson's ParadoxConfounding - LinkedInEasySoftware EngineerReview a Web Application Architecture
This question evaluates system design competencies such as scalability, reliability, fault tolerance, database architecture, load balancing, external dependency management, and observability, and is categorized under System Design.
System DesignTechnical Screen - LinkedInMediumSoftware EngineerSecond-Smallest Value in a Min Tournament Tree in Logarithmic Time
Given a full binary tournament tree stored in array order, where each internal node holds the smaller of its two children, return the second-smallest leaf value in logarithmic time. It tests reasoning about how a knockout structure constrains where the runner-up can be, array-based tree indexing, and justifying a partial search.
Coding & AlgorithmsTechnical Screen - LinkedInHardSoftware EngineerShortest Word Distance Queries
Tests designing an inverted index and using two-pointer or binary search techniques to efficiently compute the minimum absolute index distance between word occurrences across repeated queries.
Coding & AlgorithmsTechnical Screenhash-tabletwo-pointersbinary-search - LinkedInMediumSoftware EngineerSolve common string and subarray problems
Assesses core string and array manipulation skills by requiring string reversal and contiguous-subarray computations for maximum sum and maximum product, identified under the Coding & Algorithms category.
Coding & AlgorithmsTechnical Screen - LinkedInMediumMachine Learning EngineerSort the values of a quadratic function applied to a sorted array in linear time
Given an integer array sorted in ascending order and the coefficients of a quadratic function, apply the function to every element and return the results in sorted order in linear time. Tests reasoning about the shape of a parabola, handling of positive, negative and zero leading coefficients, and duplicate values.
Coding & AlgorithmsTechnical Screen - LinkedInHardMachine Learning EngineerSum Every Element of an N-Dimensional Array Readable Only by Index Lists
Compute the sum of all elements of an array with any number of dimensions when the array exposes only its dimension sizes and a get method that reads one element from a full list of indices. It tests systematic enumeration of every index combination and exact handling of sums beyond the 32-bit range.
Coding & AlgorithmsTechnical Screen - LinkedInMediumData ScientistTest If Initial Video Uploads Are Shorter Than Later Ones
Evaluates hypothesis testing for whether users' first video uploads are shorter than later uploads. Strong answers define a clean data slice, paired user-level metrics, robust tests, and SQL-style implementation.
Analytics & ExperimentationTechnical Screen - LinkedInHardMachine Learning EngineerTight-Deadline Launch, Disagreeing With a Manager, and Guiding a Junior Scientist
A three-question behavioral segment from a machine learning engineer phone screen: a project launch under a tight deadline, a disagreement with your manager, and guiding a junior scientist to finish a piece of work. It tests prioritization, constructive disagreement, mentoring, and concise STAR storytelling under time pressure.
Behavioral & LeadershipTechnical Screen - LinkedInMediumMachine Learning EngineerTransform Biased and Uniform Random Bits Exactly
Tests the ability to design and mathematically prove exact random variate generators between biased and uniform bit sources, analyzing termination and expected sample complexity.
Statistics & MathTechnical ScreenProbabilityAlgorithmsSampling - LinkedInMediumData Scientist[SQL] Job Ad Metrics with Applicant Filter
This question evaluates proficiency in SQL data manipulation and deduplication, focusing on aggregations, distinct counts, and conditional filtering of event records (views vs applies).
Data Manipulation (SQL/Python)Technical Screen - Listen LabsMediumSoftware EngineerParse a Truncated Streaming JSON Prefix of Strings and Nested Arrays
Write a function that turns a truncated prefix of streamed JSON, made only of strings and nested arrays, into the value received so far. It tests careful parsing of half-received strings, unclosed arrays and input that ends after a comma, along with writing your own edge-case tests.
Coding & AlgorithmsTechnical Screen - Listen LabsMediumSoftware EngineerReconstruct a Warehouse Pick-List From a Dropped Scanner Feed
Write a function that turns a scanner feed cut off mid-transmission into the best pick-list it can represent so far, recovering half-written labels and unfinished bin groupings. Tests careful state-machine parsing of truncated nested data.
Coding & AlgorithmsTechnical Screenparsingstacksstrings - Lowe'sMediumSoftware EngineerExplain Python internals and practices
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Explain Python internals and practices states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - Lowe'sHardSoftware EngineerExplain cloud computing fundamentals
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Explain cloud computing fundamentals states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - Lowe'sMediumSoftware EngineerFind longest unique substring
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Find longest unique substring states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - Luma AIHardSoftware EngineerImplement Refactoring Assessment Features
This problem tests your ability to manage stateful data with temporal versioning and reconstruct valid paths in a graph while ensuring data integrity under constraints.
Coding & AlgorithmsTechnical ScreenGraphsData StructuresVersioning - Luma AIHardSoftware EngineerManage Prompt and Model Behavior in Production
This question tests the ability to design an end-to-end configuration management and observability system for LLM applications that ensures reproducibility, safety, and rapid recovery from performance drift.
ML System DesignTechnical ScreenLLMOpsSystem DesignObservability - Luma AIMediumMachine Learning EngineerSolve Seven Python Image and Data Tasks
This problem tests foundational proficiency in NumPy array manipulation, mathematical function implementation, and geometric logic, which are essential for low-level image processing tasks.
Coding & AlgorithmsTechnical ScreenNumPyImage ProcessingNumerical Stability - LyftMediumData ScientistAnalyze Rider Behavior in Dynamic-Pricing Trial
Analyze Rider Behavior in Dynamic-Pricing Trial 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 - LyftMediumData ScientistCommuter Ride-Choice Probability: Committed and Permanent Riders
Tests discrete probability modeling, complementary counting, and Bayesian updating to analyze multi-day commuter behavior across distinct rider segments.
Statistics & MathTechnical Screenprobabilitybayes-theoremconditional-probability - LyftMediumData ScientistCompute Commuter Ride Probabilities
This question tests the ability to apply Bayes' Theorem and marginal probability to model commuter behavior and predict ride-hailing demand based on environmental factors.
Statistics & MathTechnical ScreenProbabilityBayesian InferenceConditional Probability - LyftEasyData ScientistDetermine Probability of Single Ride on Following Day
Evaluates a binomial probability setup for a ride-pricing experiment with two independent offers. Strong answers model the number of reasonable prices as Binomial(2, P), compute exactly one success as 2P(1-P), and compute permanent-rider probability as P squared.
Statistics & MathTechnical Screen - LyftHardData ScientistDevelop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors
Evaluates dynamic-pricing system design for Lyft's two-sided marketplace. Strong answers separate prediction from control, include real-time demand and supply features, model elasticity and ETA response, optimize with fairness and stability constraints, and validate through simulation and experiments.
Machine LearningTechnical Screen - LyftEasyData ScientistExplain Bayes’ Theorem and P-Value in Decision-Making
Explain Bayes’ Theorem and P-Value in Decision-Making 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 - LyftMediumMachine Learning EngineerExplain Transformer and Fine-Tuning Basics
This question evaluates understanding of transformer architectures, embedding models, and transfer learning concepts such as pretraining and fine-tuning, testing competence in representation learning and model adaptation.
Machine LearningTechnical Screen - LyftHardData ScientistHow to Target Coupon Users
This question evaluates a data scientist's competency in causal inference, uplift and propensity modeling, user segmentation, and experimental design for targeted marketing campaigns.
Analytics & ExperimentationTechnical Screen - LyftHardData ScientistHow to target commute coupon users?
This question tests your ability to design an experiment to measure the incremental lift of a coupon program and your strategy for segmenting users based on commuting behaviors to maximize ROI.
Analytics & ExperimentationTechnical ScreenCausal InferenceExperimentationUser Segmentation - LyftMediumMachine Learning EngineerImplement K-Means Clustering from Scratch in Plain Python or NumPy
A live coding exercise that asks you to implement k-means clustering from scratch in Python, with NumPy optional, and make it pass the interviewer's test cases. It tests the assignment and update steps, initialization, convergence, empty clusters and writing runnable code under time pressure.
Machine LearningTechnical Screen
LinkedIn
Luma AI
Lyft