System Design interview questions

Showing 1201–1230 of 1311 results

• Design synchronization for water molecule assemblyMedium

  This question evaluates concurrency and synchronization skills, including thread coordination, use of locks/semaphores/condition variables, and reasoning about safety and liveness properties in concurrent systems.

  TeslaSoftware EngineerSystem DesignHR Screen
• From a Monorepo Commit to an Over-the-Air Vehicle Update, and Choosing What to BuildHard

  Trace everything that happens between an engineer pushing a commit to a monorepo and the resulting software being installed on customers' cars, then explain how the monorepo decides which targets to build and test and in what order. It tests CI and release gating, artifact provenance, staged over-the-air rollout, safe in-vehicle installation, and dependency-graph builds.

  TeslaSoftware EngineerSystem DesignOnsite
• Design a Warehouse for Key MetricsHard

  This question evaluates a data engineer's skills in dimensional modeling, data warehousing, and analytics infrastructure, covering facts and dimensions, grain definition, surrogate keys, partitioning and clustering strategies, and operational concerns like deduplication, late-arriving events, null/anonymous users, and slowly changing dimensions.

  TikTokData EngineerSystem DesignTechnical Screen
• Design a distributed key-value storeMedium

  This problem tests your ability to design a scalable, fault-tolerant distributed key-value store, focusing on partitioning strategies, replication, and handling node failures.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Design a high-concurrency ticketing systemHard

  TikTok software-engineer system-design interview question: design a high-concurrency ticketing / flash-sale system that survives millions of concurrent purchase attempts with zero oversell. It tests admission control and fairness (virtual waiting room, FIFO vs lottery), atomic inventory allocation with TTL reservations, anti-bot defenses, idempotent order creation, hot-key sharding, the data model and APIs, caching, message queues and delivery semantics, consistency and reconciliation, failure handling, capacity planning, SLOs, load testing, and monitoring.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Design and explain your project architectureHard

  A TikTok software-engineer system-design screen that asks you to walk through one of your recent projects end-to-end: architecture diagram, components and data stores, sync vs. async data flows and protocols, scaling and capacity planning, fault tolerance, backpressure, idempotency, observability, and security. It also probes the design trade-offs you made, the real failure modes you hit, the measurable impact, and what you would redesign. Includes a fully worked reference answer plus the scoring rubric.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Design low-latency large-scale hotel booking systemMedium

  This question evaluates understanding of large-scale backend and distributed system design, including scalability, high availability, consistency models, concurrency control to prevent double-booking, hotspot mitigation, and protocol trade-offs for low-latency booking and availability flows.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Design overload protection with load sheddingHard

  Design overload protection with load shedding 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.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Design tables from metricsHard

  This question evaluates analytics data modeling and event-driven warehousing skills, including schema design, metric derivation, partitioning/clustering strategies, and data-quality concerns such as deduplication, late arrivals, and idempotency.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Explain SRE architecture and troubleshooting scenariosHard

  This question evaluates a candidate's competency in SRE architecture and reliability engineering, covering Kubernetes fundamentals, pod failure troubleshooting, SLO/SLA/SLI reasoning, multi-layer latency diagnosis, and SQL versus NoSQL trade-offs within the System Design domain.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Explain Your System ArchitectureHard

  Explain Your System Architecture 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.

  TikTokSoftware EngineerSystem DesignOnsite
• Improve and measure service performanceHard

  This question evaluates performance engineering and observability skills—specifically SLIs/SLOs definition, instrumentation and profiling, load-testing, bottleneck identification, and optimization trade-offs for stateless HTTP microservices.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Design a Restaurant Seat and Table Reservation SystemHard

  A system design question asking for a restaurant seat reservation system in which diners search availability by party size and time, then book, change or cancel. It tests the table-and-time inventory model, preventing double booking under concurrent requests, holds, availability caching and partitioning by restaurant.

  ToastSoftware EngineerSystem DesignTechnical Screen
• Design a GPU-aware pod schedulerHard

  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 Design a GPU-aware pod scheduler states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Together AISoftware EngineerSystem DesignTechnical Screen
• Explain Go concurrency and database choicesHard

  This question evaluates a candidate's understanding of Go concurrency primitives and scheduler behavior, cross-language concurrency comparisons, database model trade-offs (relational, document, columnar, key–value), and end-to-end storage design for high-concurrency systems.

  TripStackSoftware EngineerSystem DesignTechnical Screen
• Design Interviewer Availability and Atomic Booking APIsMedium

  Design a relational data model and concurrent APIs for managing interviewer availability and atomic candidate interview booking.

  TruefoundrySoftware EngineerSystem DesignOnsite
• Design trip booking REST API and schemaMedium

  This question evaluates RESTful API design, temporal data modeling, inventory management strategies, request/response/status-code design, data schema and indexing, and handling booking lifecycle changes such as extensions.

  TuroSoftware EngineerSystem DesignTechnical Screen
• Plan a major database version upgradeMedium

  This question evaluates competency in database upgrade planning, including data migration strategies, cutover coordination, compatibility considerations, change-data-capture versus dual-write trade-offs, and rollback and risk management.

  TuroSoftware EngineerSystem DesignTechnical Screen
• Route HTTP requests to pods in a clusterMedium

  This question evaluates understanding of request routing, service discovery, virtual service abstractions, load balancing algorithms, health checks, and fault-tolerance mechanisms within containerized cluster environments, testing competencies in distributed systems and networked service architecture.

  TuroSoftware EngineerSystem DesignTechnical Screen
• Design and Scale Quantitative Optimization WorkflowsMedium

  Design a scalable, deterministic quantitative optimization and backtesting platform capable of executing 30,000 portfolio optimization runs with strict point-in-time data correctness and full data lineage.

  Two SigmaSoftware EngineerSystem DesignTechnical Screen
• Compare Paxos and RaftHard

  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.

  UberSoftware EngineerSystem DesignOnsite
• Design A URL ShortenerMedium

  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.

  UberSoftware EngineerSystem DesignOnsite
• Design Distributed Storage for Large Structured DataHard

  Design distributed storage with versioned metadata, block replicas, layered caches, failure repair, locality-aware jobs, and resource-aware load balancing.

  UberSoftware EngineerSystem DesignOnsite
• Design Food Delivery CartHard

  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.

  UberSoftware EngineerSystem DesignOnsite
• Design MapReduce for schedule aggregationMedium

  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.

  UberSoftware EngineerSystem DesignOnsite
• Design MapReduce for schedulesHard

  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.

  UberSoftware EngineerSystem DesignOnsite
• Design Product Page View TrackingMedium

  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.

  UberSoftware EngineerSystem DesignOnsite
• Design Stock Price AlertsMedium

  This question evaluates a candidate's ability to design scalable, low-latency alerting and notification systems, covering system architecture, APIs, data modeling, event-driven alert evaluation, deduplication, and multi-channel delivery; it is in the System Design category and assesses practical application skills.

  UberSoftware EngineerSystem DesignTechnical Screen
• Design Uber Eats-style search functionHard

  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.

  UberSoftware EngineerSystem DesignOnsite
• Design a Distributed Logging SystemMedium

  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.

  UberSoftware EngineerSystem DesignOnsite