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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 2161–2190 of 4616 results
- LinkedInEasyData ScientistAnalyze member video posting behavior by country
A LinkedIn data scientist technical-screen question on member video-posting behavior using two relational tables. It tests joins, first-event derivation with MIN/GROUP BY, conditional grouping for US vs non-US comparison, and distinct-member counts per country with a length filter.
Data Manipulation (SQL/Python)Technical Screen - LinkedInEasySoftware EngineerAnswer Repeated Shortest Word Distance Queries
Design a data structure that preprocesses a list of words to efficiently answer repeated queries for the shortest index distance between two distinct words.
Coding & AlgorithmsTechnical Screenhash-tabletwo-pointersdesign - LinkedInEasySoftware EngineerArrange Nonnegative Integers to Form the Largest Number
Tests custom sorting logic and string manipulation to arrange a list of nonnegative integers into the lexicographically largest combined numerical string.
Coding & AlgorithmsTechnical Screensortinggreedystring - LinkedInMediumSoftware EngineerCan You Place N Objects?
This problem tests your ability to handle greedy array traversal while maintaining specific adjacency constraints, requiring careful management of boundary indices.
Coding & AlgorithmsTechnical ScreenArraysGreedySimulation - LinkedInMediumSoftware EngineerCheck Whether a String Is a Signed Decimal Number Without Exponent Notation
Write a function that decides whether a string is a valid plain decimal number with an optional sign and fractional part, rejecting exponents, stray symbols, and repeated decimal points. It tests turning a small grammar into careful character-by-character validation and covering edge cases such as empty strings and bare signs.
Coding & AlgorithmsTechnical Screen - LinkedInEasyData ScientistChoose better bank queue and describe distributions
This question tests your ability to apply basic queueing theory to compare waiting times and your ability to reason about how distributional shape affects central tendency measures.
Statistics & MathTechnical ScreenQueueing TheoryProbabilityStatistics - LinkedInMediumSoftware EngineerCompare heap and stack memory
This question evaluates understanding of memory management concepts including stack versus heap allocation, object lifetimes, allocation and free behavior, access patterns, performance characteristics, and thread visibility within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - LinkedInMediumData ScientistCompare queueing systems and common distributions
LinkedIn data scientist statistics screen covering pooled versus separate queues, height distributions and mixture distributions, right-skewed network degree, mean-median-mode ordering, and L1/L2 regularization bias.
Statistics & MathTechnical Screen - LinkedInEasyData ScientistCompute each member’s current notification status
A LinkedIn data scientist SQL screen: given an end-of-January notification-status snapshot and a February log of turn_on/turn_off actions, compute each member's current status as of 2020-02-29, keeping members from either table. Follow-ups explore reconstructing state when the action log has no date column — unsolvable in general, but recoverable via action-count parity when every action is a guaranteed valid transition.
Data Manipulation (SQL/Python)Technical Screen - LinkedInEasyMachine Learning EngineerCompute point-to-segment minimum distance
This problem tests your ability to apply vector projections and geometric constraints to find the shortest distance from a point to a line segment in a 2D plane.
Coding & AlgorithmsTechnical ScreenGeometryVectorsMath - LinkedInMediumMachine Learning EngineerCompute total covered interval length
Compute total covered interval length 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 - LinkedInMediumData ScientistCount Article Types Viewed
Count Article Types Viewed evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Data Manipulation (SQL/Python)Technical Screen - LinkedInMediumData ScientistDerive expectation for two consecutive heads
This question evaluates mastery of probability theory and stochastic processes, specifically expectation and variance calculations, Markov-chain or recurrence reasoning, and pattern waiting times in Bernoulli trials relevant for a Data Scientist role.
Statistics & MathTechnical Screen - LinkedInMediumData ScientistDerive mean and variance of x̄
This question tests your ability to derive the expected value and variance of the sample mean for a sequence of random variables, accounting for the effect of correlation between them.
Statistics & MathTechnical ScreenStatisticsVarianceExpectation - LinkedInEasySoftware EngineerDesign O(1) Randomized Multiset
This problem tests your ability to design a data structure that supports efficient insertion, deletion, and uniform random sampling by combining dynamic arrays with hash maps to achieve constant time complexity.
Coding & AlgorithmsTechnical ScreenHash MapArrayDesign - LinkedInMediumSoftware EngineerDesign a Calendar System
Tests your ability to design a scalable calendar backend that supports single and recurring events, attendee invitations, and time-based reminder notifications with timezone awareness.
System DesignTechnical ScreenDistributed SystemsData ModelingScheduling - LinkedInHardMachine Learning EngineerDesign a Model That Predicts Whether a Member Clicks a Displayed Job
An ML system design question: predict whether a member will click a job displayed to them, using member features, job content and interaction history. It tests defining the supervision signal and training data, choosing a loss, preventing data leakage, building member and job embeddings, and laying out the training and serving components.
ML System DesignTechnical Screen - LinkedInHardSoftware EngineerDesign a Multi-Region Malicious-IP Detection System
Design a globally distributed, low-latency edge security service that detects and blocks malicious IP addresses across regions, featuring decentralized evaluation, asynchronous intelligence sharing, and rapid false-positive rollback.
System DesignTechnical ScreenDistributed SystemsSecurityLow Latency - LinkedInMediumMachine Learning EngineerDesign a Personalized Feed and Compare Ranking Objectives
Design an end-to-end personalized professional feed system and compare pointwise, pairwise, and listwise learning-to-rank formulations across offline training, online serving, and business metrics.
ML System DesignTechnical Screenml-system-designrecommendation-systemslearning-to-rank - LinkedInMediumSoftware EngineerDesign a Raffle Drum That Draws Duplicate Tickets Fairly in O(1)
Design a drum that holds raffle ticket codes, where the same code can be dropped in more than once, and supports adding a ticket, pulling a specific ticket out, and drawing one at random so every physical ticket in the drum is equally likely to come out — all in average O(1) time. Tests combining an array with a hash index so removal and random draw both stay constant time even with string keys and duplicates.
Software Engineering FundamentalsTechnical Screendesignhash-maprandomization - LinkedInMediumSoftware EngineerDesign a Randomized Multiset
This question evaluates the ability to design and implement a randomized multiset that correctly handles duplicate integer occurrences while providing expected O(1) insert, remove, and uniform getRandom operations, testing knowledge of data structures, randomness, and performance constraints.
Coding & AlgorithmsTechnical Screen - LinkedInMediumSoftware EngineerDesign a Top-K search words service
The question evaluates understanding of scalable, low-latency analytics and search-event systems, including high-throughput event ingestion, Top-K aggregation across all-time and sliding windows, normalization/deduplication trade-offs, and multi-region consistency considerations.
System DesignTechnical Screen - LinkedInHardMachine Learning EngineerDesign a distributed key-value store
Design a distributed key-value store evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - LinkedInEasyData ScientistDesign a short-video recommender system
This question assesses the ability to architect a multi-stage machine learning pipeline for a short-video feed, covering candidate generation, ranking, and performance evaluation.
Machine LearningTechnical ScreenSystem DesignRecommendationRanking - LinkedInMediumSoftware EngineerDesign a stack with max removal
This question evaluates data-structure design and algorithmic complexity analysis by requiring a stack-like structure that supports standard stack operations plus efficient maximum retrieval and targeted removal, assessing practical implementation skills and time-complexity reasoning at a practical application level.
Coding & AlgorithmsTechnical Screen - LinkedInMediumMachine Learning EngineerDesign an ML Forecasting and Pacing System for Ad Bidding
Design an ad forecasting and budget pacing architecture that combines predictive ML with deterministic budget guardrails to optimize campaign delivery across non-stationary traffic.
ML System DesignTechnical Screenml-system-designad-techtime-series - LinkedInHardSoftware EngineerDesign distributed parallel job processing
This question evaluates understanding of distributed systems architecture, parallel job scheduling, fault tolerance, coordination and concurrency control, API and data model design, idempotency, observability, and capacity planning for multi-tenant, heterogeneous workloads.
System DesignTechnical Screen - LinkedInMediumData ScientistDevice Status without Timestamps
This question evaluates data manipulation and behavioral analytics competencies, emphasizing aggregation, deduplication, distinct counts, and date-based filtering to measure author and viewer activity.
Data Manipulation (SQL/Python)Technical Screen - LinkedInEasyData ScientistDo US members upload more videos than non-US?
This question evaluates a data scientist's competency in analytics and experimentation, specifically metric design, confounder control, cohorting, and statistical inference for comparing user behavior across segments.
Analytics & ExperimentationTechnical Screen - LinkedInMediumMachine Learning EngineerExplain Core ML Fundamentals
This question assesses core conceptual knowledge of supervised learning, optimization strategies, and regularization techniques used in model training.
Machine LearningTechnical ScreenOptimizationRegularizationDecision Trees
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