practice a little every day, paws crossed ♡
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 4501–4530 of 9403 results
- 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 Ranked Social Feed with Hybrid Fan-Out
Design a scalable, read-heavy ranked social feed supporting millions of users. The system must balance fan-out-on-write and fan-out-on-read while ensuring read-your-own-writes consistency for authors.
System DesignOnsiteDistributed SystemsCachingSocial Network - LinkedInMediumSoftware EngineerDesign a Scalable Calendar Service
Design calendar event storage and time-range reads, explain concurrent edits and scaling, and clarify recurrence and notification requirements.
System DesignOnsite - LinkedInMediumSoftware EngineerDesign a Scalable Error Message Counting Service
Design a service that receives application error messages and reports how often each distinct error occurs over time. It tests grouping varied messages into error signatures, absorbing bursty write traffic during incidents, time-windowed aggregation, top-error queries and count accuracy under failures.
System DesignOnsite - LinkedInMediumMachine Learning EngineerDesign a Skills inference system
This question assesses the ability to build an end-to-end ML pipeline for extracting and normalizing professional skills from unstructured user-generated text, such as job descriptions or member profiles.
ML System DesignOnsiteNLPEntity LinkingRecommendation Systems - LinkedInEasySoftware EngineerDesign a Top-K Ranking Service
This question tests the ability to design a distributed, low-latency system capable of maintaining and serving a real-time 'Top-K' list of items (e.g., trending hashtags) across a massive stream of incoming data.
System DesignOnsiteScalabilityStreamingCaching - 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 - LinkedInMediumSoftware EngineerDesign a company-wide monitoring system
This question evaluates skills in designing large-scale observability and monitoring systems, covering telemetry collection (metrics, logs, traces), high-throughput ingestion, storage and scaling strategies, alerting pipelines, and operational trade-offs like cardinality, retention, and backpressure.
System DesignOnsite - 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 - LinkedInMediumSoftware EngineerDesign a malicious-URL checking service using an isMalicious API
This question tests your ability to design a high-throughput, low-latency URL reputation checking service, focusing on caching strategies, scalability, and system resilience.
System DesignOnsiteCachingScalabilityResilience - LinkedInMediumSoftware EngineerDesign a max-stack with efficient operations
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Design a max-stack with efficient operations states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - LinkedInMediumSoftware EngineerDesign a meeting scheduler API
This problem tests your ability to design a thread-safe scheduling system using interval management techniques and efficient data structures for range querying.
Software Engineering FundamentalsOnsiteObject-Oriented DesignConcurrencyIntervals - LinkedInMediumSoftware EngineerDesign a metrics platform without alerting
This problem tests your ability to design a high-throughput time-series metrics system, focusing on ingestion pipelines, efficient storage schema for multi-dimensional data, and query performance.
System DesignOnsiteTime-SeriesScalabilityData Modeling - LinkedInHardSoftware EngineerDesign a scalable calendar system
This question evaluates a candidate's ability to design a scalable, multi-tenant calendar platform, testing competencies in distributed systems architecture, API and data-model design for events/recurrence/time zones, access control and privacy boundaries, notification pipelines, and external integration.
System DesignOnsite - LinkedInMediumSoftware EngineerDesign a scalable key-value store
This system design question evaluates expertise in distributed systems and scalable storage architecture within the System Design domain, focusing on data partitioning, replication, durability, consistency models, and low-latency operation.
System DesignOnsite - 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 a system for LinkedIn Skills
This question evaluates a machine learning engineer's ability to design an end-to-end ML system for skill inference and recommendation on a professional networking platform, testing competencies in data sourcing and labeling, feature engineering, model selection (including LLM integration), online serving, monitoring, and quality control.
ML System DesignOnsite - 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 - LinkedInMediumSoftware EngineerDesign an exception monitoring system with top‑K
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.
System DesignOnsiteStreamingScalabilityData Modeling - LinkedInMediumSoftware EngineerDesign an in-memory key-value store using maps
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.
System DesignOnsite - 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 - LinkedInMediumSoftware EngineerDesign scalable job scheduler and query dashboard
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.
System DesignOnsite - 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 - LinkedInMediumData ScientistDiagnose a Drop in Applications from Job Recommendation Emails
Diagnose falling applications from job emails by tracing funnel rates, recommendation relevance, promoted-job mix, and ranking changes.
Analytics & ExperimentationOnsite - LinkedInMediumSoftware EngineerDiscuss Projects and Tradeoffs
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.
Behavioral & LeadershipOnsite - 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 - LinkedInMediumData ScientistEstimate Redesign Impact Using Propensity Score Matching
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.
Analytics & ExperimentationOnsite - LinkedInMediumData ScientistEstimate an Experiment’s Mean Effect from Cohort and Metric Tables
Estimate experimental impact from cohort and timestamped metric data using user-level outcomes, difference in means, confidence intervals, and valid testing.
Statistics & MathOnsite - 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
LinkedIn