LinkedIn interview questions

Showing 61–90 of 154 results

• Design an exception monitoring system with top‑KMedium

  This question tests the ability to design a high-throughput distributed system capable of ingesting, deduplicating, and aggregating exception logs to provide real-time top-K statistics.

  LinkedInSoftware EngineerSystem DesignOnsite
• Design an in-memory key-value store using mapsMedium

  This question evaluates skills in in-memory data modeling, concurrent access control, time-complexity reasoning for expected O(1) operations, and storage engine considerations such as durability, compaction, and TTL when restricting indexing to map/dictionary primitives.

  LinkedInSoftware EngineerSystem DesignOnsite
• Design distributed parallel job processingHard

  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.

  LinkedInSoftware EngineerSystem DesignTechnical Screen
• Design scalable job scheduler and query dashboardMedium

  This question evaluates a candidate's system design skills in building a scalable, fault-tolerant job scheduling system and efficient near-real-time time-window querying, focusing on distributed scheduling, storage and indexing strategies, partitioning, and reliability guarantees, and is commonly asked to probe trade-offs among scalability, latency, availability, and consistency. Belonging to the System Design domain, it examines both high-level architectural thinking and practical application-level considerations such as data modeling, query/index design, operational concerns like hot partitions, and horizontal scaling.

  LinkedInSoftware EngineerSystem DesignOnsite
• Device Status without TimestampsMedium

  This question evaluates data manipulation and behavioral analytics competencies, emphasizing aggregation, deduplication, distinct counts, and date-based filtering to measure author and viewer activity.

  LinkedInData ScientistData Manipulation (SQL/Python)Technical Screen
• Diagnose a Drop in Applications from Job Recommendation EmailsMedium

  Diagnose falling applications from job emails by tracing funnel rates, recommendation relevance, promoted-job mix, and ranking changes.

  LinkedInData ScientistAnalytics & ExperimentationOnsite
• Discuss Projects and TradeoffsMedium

  English summary: This question evaluates competency in technical ownership, system architecture articulation, trade-off analysis, measurable outcome reporting, and cross-team leadership within software engineering projects.

  LinkedInSoftware EngineerBehavioral & LeadershipOnsite
• Do US members upload more videos than non-US?Easy

  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.

  LinkedInData ScientistAnalytics & ExperimentationTechnical Screen
• Estimate Redesign Impact Using Propensity Score MatchingMedium

  Estimate Redesign Impact Using Propensity Score Matching evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  LinkedInData ScientistAnalytics & ExperimentationOnsite
• Estimate an Experiment’s Mean Effect from Cohort and Metric TablesMedium

  Estimate experimental impact from cohort and timestamped metric data using user-level outcomes, difference in means, confidence intervals, and valid testing.

  LinkedInData ScientistStatistics & MathOnsite
• Explain Core ML FundamentalsMedium

  This question assesses core conceptual knowledge of supervised learning, optimization strategies, and regularization techniques used in model training.

  LinkedInMachine Learning EngineerMachine LearningTechnical Screen
• Explain a Difficult Technical Decision and Its ImplementationMedium

  Discuss a project's technical trade-off through concrete mechanisms, failure cases, validation evidence, and the strengths of rejected alternatives.

  LinkedInSoftware EngineerBehavioral & LeadershipOnsite
• Explain a Project with a Diagram and Deep Dive into Your DesignMedium

  Communicate a project through a simple data-flow diagram, then explain design choices, implementation ownership, failure behavior, and validation.

  LinkedInSoftware EngineerSoftware Engineering FundamentalsOnsite
• Explain a Technical Project with an Architecture DiagramHard

  This question evaluates your ability to clearly explain a complex software architecture, articulate your personal technical ownership, justify design trade-offs, and quantify business and operational impact.

  LinkedInSoftware EngineerBehavioral & LeadershipTechnical Screen
• Explain a past project and critique a prior teamMedium

  This question evaluates communication of technical ownership, system architecture reasoning, and leadership competencies such as collaboration, conflict resolution, and influencing change within a Software Engineer role.

  LinkedInSoftware EngineerBehavioral & LeadershipTechnical Screen
• Explain database transactions and ACIDMedium

  This question evaluates a candidate's understanding of database transactions including ACID properties, isolation levels (read uncommitted, read committed, repeatable read, serializable), common anomalies (dirty reads, non-repeatable reads, phantom reads), MVCC, and commit/rollback semantics, testing competency in transactional correctness and concurrency control. Commonly asked in System Design and database contexts because it probes reasoning about data integrity, visibility, and durability under concurrent workloads, and it tests both conceptual understanding of transactional guarantees and practical application of isolation behavior in relational database systems.

  LinkedInSoftware EngineerSystem DesignTechnical Screen
• Explain gradient descent, learning rate, backpropagation, overfitting and batch sizeMedium

  A machine learning fundamentals screen on how neural networks are trained: how gradient descent updates parameters, what the learning rate and batch size change, how backpropagation computes gradients, and how to diagnose and fix underfitting and overfitting. Tests mechanics and practical training judgment, not definitions alone.

  LinkedInMachine Learning EngineerMachine LearningTechnical Screen
• Explain overfitting vs underfitting and fixesEasy

  This question tests the conceptual understanding of the bias-variance tradeoff and the practical strategies used to improve model generalization.

  LinkedInMachine Learning EngineerMachine LearningTechnical Screen
• Explain the Design of a Recent FeatureMedium

  Tests your ability to communicate software design clearly by breaking down a production feature into its architecture, trade-offs, individual ownership, and verification strategy.

  LinkedInSoftware EngineerSoftware Engineering FundamentalsOnsite
• Explain variance reduction in random forestsMedium

  This question tests your conceptual understanding of how Random Forests reduce the variance of base decision trees through bagging and feature randomness.

  LinkedInData ScientistMachine LearningTechnical Screen
• Explain your most impactful project clearlyMedium

  This question evaluates technical communication, system design, architectural reasoning and leadership competencies by requiring a clear description of system components, data flow, individual responsibilities, trade-offs, and measurable impact.

  LinkedInSoftware EngineerBehavioral & LeadershipOnsite
• Explore an Unknown Grid and Return a Robot PathMedium

  This problem tests graph traversal, state-space representation, and pathfinding in an environment where the map must be discovered dynamically.

  LinkedInDevOps EngineerSoftware Engineering FundamentalsOnsite
• Fewest-Edge Distance Between Two Nodes in a Directed Acyclic GraphMedium

  Given a directed acyclic graph and two nodes, return the fewest edges on a directed path from the source to the target, 0 when they are the same node, or -1 when the target cannot be reached. Asked as a follow-up to topological ordering, it tests traversal of dependency graphs.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Find a Target's Index in a Rotated Sorted Array of Distinct IntegersMedium

  Find the index of a target value in an array of distinct integers that was sorted ascending and then rotated at an unknown pivot, returning -1 when it is absent. Tests adapting search to a partially ordered array in logarithmic time and handling unrotated and single-element inputs.

  LinkedInSoftware EngineerCoding & AlgorithmsOther
• Find dictionary words matching a phone digit stringMedium

  This problem tests your ability to map phone keypad digits to characters and efficiently filter a provided dictionary to find words that match a specific digit sequence.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Find dictionary words that match a phone keypad digit sequenceEasy

  Coding problem: given a list of known words and a digit string typed on a standard phone keypad, return every known word whose letters map to exactly that digit sequence, in sorted order. It tests string encoding, choosing a lookup structure for the dictionary, and analyzing time complexity.

  LinkedInSoftware EngineerCoding & AlgorithmsTechnical Screen
• Find index with positive suffix sumsMedium

  This problem tests your ability to optimize array traversals using prefix or suffix sums, specifically evaluating your skill in calculating cumulative values efficiently in O(n) time.

  LinkedInData ScientistCoding & AlgorithmsTechnical Screen
• Find lowest common ancestorMedium

  This question evaluates understanding of binary tree structure, lowest common ancestor concepts, and competency in algorithmic reasoning and tree traversal within the Coding & Algorithms category.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Find shortest word transformation with cachingMedium

  Find shortest word transformation with caching 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.

  LinkedInMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Find the K Closest Values in a Sorted ArrayMedium

  This problem tests binary search and window optimization to find the k nearest elements to a target in a sorted integer array.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite