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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.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 3361–3390 of 3640 results
- UberHardSoftware EngineerCheck feasibility of AI course schedule
This question evaluates competency in analyzing directed graphs and dependency resolution, focusing on cycle detection and the feasibility of ordering prerequisites for course completion.
Coding & AlgorithmsOnsite - UberMediumMachine Learning EngineerChoose K pickup locations minimizing L1 distance
This question evaluates facility-location and spatial optimization skills under the Manhattan (L1) metric, testing algorithm design, combinatorial optimization, and handling geometric data.
Coding & AlgorithmsOnsite - UberHardSoftware EngineerCompare Paxos and Raft
This question evaluates understanding of distributed consensus protocols, replicated logs, linearizability, fault tolerance, and operational concerns such as leader election, quorum semantics, log replication, membership changes, and failure recovery.
System DesignOnsite - UberMediumMachine Learning EngineerCompute minimal time to finish dependent tasks
This question evaluates understanding of graph algorithms and scheduling, specifically reasoning about task dependencies, parallel execution, and cycle detection in directed graphs.
Coding & AlgorithmsOnsite - UberHardSoftware EngineerCount Islands After Each Land Addition to an All-Water Grid
A grid coding problem where land cells are added one at a time to an initially all-water grid and you must report the number of islands after every addition. It tests incremental connectivity, efficient merging of islands as they join, and correct handling of repeated additions.
Coding & AlgorithmsOnsite - UberMediumSoftware EngineerCount Islands After Land Additions
This question tests the ability to manage connected components in a grid dynamically, specifically requiring an efficient approach to track changes after incremental land additions.
Coding & AlgorithmsOnsiteGraphsDSUData Structures - UberHardSoftware EngineerCount Islands of Connected Land Cells in a Binary Grid
A classic grid coding problem asking you to count the islands formed by horizontally or vertically connected land cells in a binary grid. It tests grid traversal, connected-component counting, and careful handling of grid boundaries and already visited cells.
Coding & AlgorithmsOnsite - UberMediumSoftware EngineerCount Valid Infection Orders
This problem tests your ability to use combinatorial counting and dynamic programming to determine the number of valid sequences in which a population can be infected under specific adjacency constraints.
Coding & AlgorithmsOnsiteDynamic ProgrammingCombinatoricsAlgorithms - UberMediumMachine Learning EngineerCount connected delivery zones
This question evaluates graph traversal and connected-components detection on grids, measuring competence in grid-based algorithms, neighbor connectivity, and algorithmic analysis of time and space complexity.
Coding & AlgorithmsOnsite - UberMediumSoftware EngineerDefine and integrate room ranking factors
Define and integrate room ranking factors 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 - UberMediumData ScientistDescribe Conflict and Impact
This question set evaluates leadership, communication, stakeholder management, decision-making under ambiguity, and technical project ownership for a data scientist, including the ability to quantify impact and reflect on failures.
Behavioral & LeadershipOnsite - UberMediumSoftware EngineerDescribe a Trade-off Design Change
This question evaluates system design trade-off decision-making, technical leadership, and communication by probing how an individual balances competing goals and constraints when changing an architecture.
Behavioral & LeadershipOnsite - UberMediumData ScientistDescribe ownership and failure
This question evaluates ownership, judgment, self-awareness, and the ability to communicate technical depth to non-experts and senior stakeholders, while also probing data-science-specific competencies such as forecasting, experimentation, causal inference, and measuring impact.
Behavioral & LeadershipOnsite - UberMediumSoftware EngineerDesign A URL Shortener
This question tests your ability to design a high-scale, distributed URL shortening service that handles billions of requests while ensuring low latency and data durability.
System DesignOnsiteDistributed SystemsCachingScalability - UberHardSoftware EngineerDesign Distributed Storage for Large Structured Data
Design distributed storage with versioned metadata, block replicas, layered caches, failure repair, locality-aware jobs, and resource-aware load balancing.
System DesignOnsite - UberHardSoftware EngineerDesign Food Delivery Cart
This question tests the ability to design a distributed, highly concurrent shopping cart service for a food delivery platform, focusing on transactional integrity, race conditions, and eventual consistency.
System DesignOnsiteConcurrencyDistributed SystemsCaching - UberMediumSoftware EngineerDesign MapReduce for schedule aggregation
Design MapReduce for schedule aggregation 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 DesignOnsite - UberHardSoftware EngineerDesign MapReduce for schedules
This question evaluates proficiency in scalable stream and batch data processing, windowed aggregation, event-time semantics, deduplication, skew mitigation, and distributed computation using MapReduce/Spark paradigms; it falls under System Design and Big Data/stream-processing and is commonly asked to assess designing correct, high-throughput windowed aggregations and handling late or duplicated events at scale. The level of abstraction is practical application with architectural and operational considerations rather than low-level coding, and this English summary frames the competency and interview focus for search engines.
System DesignOnsite - UberMediumSoftware EngineerDesign Product Page View Tracking
This question evaluates a candidate's ability to design scalable, reliable event-collection and analytics systems, covering client- and server-side instrumentation, event schemas/APIs, counting total versus unique views, handling high write volumes for popular products, storage choices for raw events and aggregated counters, and operational concerns such as bot filtering, deduplication, privacy, fraud prevention, freshness, reliability, and monitoring. It is commonly asked in the System Design domain to assess architectural thinking and operational trade-offs, testing both conceptual understanding of distributed systems and practical application of scalable data pipelines, storage, and observability.
System DesignOnsite - UberMediumData ScientistDesign Rideshare Marketplace Causal Analyses
This question evaluates a data scientist's skills in experimental design and causal inference, marketplace metrics and diagnostics, fairness and safety impact assessment, incentive and policy evaluation, and ETA modeling and forecast quality measurement.
Analytics & ExperimentationOnsite - UberHardSoftware EngineerDesign Uber Eats-style search function
An Uber Software Engineer system design onsite question: design the search function for an Uber Eats-style food delivery platform, covering APIs, data ingestion, geo-aware indexing and retrieval, multi-stage ranking, real-time availability updates, scaling, caching, fault tolerance, monitoring, and freshness-vs-latency trade-offs. It tests the ability to build a low-latency, location-aware, highly available search system with strong observability.
System DesignOnsite - UberMediumSoftware EngineerDesign a Distributed Logging System
This problem evaluates the ability to design a high-throughput, fault-tolerant logging infrastructure capable of ingesting and storing massive volumes of telemetry data from diverse distributed services.
System DesignOnsiteDistributed SystemsScalabilityData Pipeline - UberMediumSoftware EngineerDesign a Driver Leaderboard Based on Reviews
This problem tests the ability to design a scalable, low-latency leaderboard system that handles high-frequency write throughput from reviews and supports read-heavy leaderboard queries.
System DesignOnsiteScalabilityCachingData Modeling - UberMediumSoftware EngineerDesign a Food Delivery Cart
This question tests the ability to design a distributed, low-latency, and fault-tolerant shopping cart service for a high-concurrency food delivery platform.
System DesignOnsiteDistributed SystemsAPI DesignCaching - UberMediumSoftware EngineerDesign a Highly Available URL Shortener
This question tests your ability to design a distributed, highly available system that handles high-read traffic and ensures data integrity for short-URL mappings.
System DesignOnsiteScalabilityCachingDatabases - UberMediumSoftware EngineerDesign a Parking Garage Object Model
This question evaluates object-oriented design, domain modeling, data structure selection, and concurrency control for resource allocation in a multi-level parking garage.
Software Engineering FundamentalsOnsite - UberMediumSoftware EngineerDesign a Parking Lot
This problem tests object-oriented design principles and the ability to manage resource allocation and concurrency in a multi-threaded environment.
Software Engineering FundamentalsOnsiteObject-Oriented DesignConcurrencySystem Design - UberMediumSoftware EngineerDesign a Real-Time Chat System
This question tests the ability to design a highly available and scalable real-time chat service, focusing on persistent connections, message routing, and reliable delivery mechanisms.
System DesignOnsiteWebSocketsDistributed SystemsScalability - UberMediumSoftware EngineerDesign a Real-Time Nearby-Driver Service
Tests the ability to architect a high-throughput, low-latency geospatial ingestion and query service that tracks millions of moving entities, handles state transitions, and avoids boundary/hot-spot bottlenecks.
System DesignOnsiteGeospatialDistributed SystemsIn-Memory Caching - UberMediumSoftware EngineerDesign a meeting room reservation API
This problem tests your ability to design an in-memory scheduling system that efficiently manages room availability and handles time-interval overlaps.
Software Engineering FundamentalsOnsiteSystem DesignInterval SchedulingData Structures
Uber