System Design interview questions

Showing 781–810 of 1311 results

• Design Ad Frequency and Order TrackingMedium

  This question tests the ability to design a high-throughput, low-latency distributed counter service for frequency capping and an accurate tracking system for ad order delivery.

  NetflixSoftware EngineerSystem DesignOnsite
• Design Netflix viewport deduplicationMedium

  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 Netflix viewport deduplication states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  NetflixSoftware EngineerSystem DesignTechnical Screen
• Design Publisher Configuration RulesHard

  This question evaluates system design skills focused on configuration management and rule-based policy modeling for supply-side advertising platforms, covering inventory modeling, rule inheritance and overrides, conflict resolution, real-time evaluation, and operational controls like validation, versioning, rollout, and auditing.

  NetflixSoftware EngineerSystem DesignOnsite
• Design Rolling-Window Ad Frequency CappingMedium

  This question evaluates system-design skills for building scalable, low-latency services that enforce time-based rolling-window state, including data modeling for impression history, read/write paths, concurrency control, and fault tolerance; category: System Design.

  NetflixSoftware EngineerSystem DesignOnsite
• Design a Distributed Frequency-Capping ServiceMedium

  Tests the ability to design an ultra-low latency, highly available counting and rate-limiting system for ad frequency capping across distributed environments, balancing consistency with latency trade-offs.

  NetflixSoftware EngineerSystem DesignOnsite
• Design a File Backup SystemHard

  This question evaluates understanding of scalable system design, file synchronization and metadata handling, data consistency, fault tolerance, and performance trade-offs involved in designing a file backup system.

  NetflixSoftware EngineerSystem DesignTechnical Screen
• Design a Job Scheduler for ML Training and Batch Inference on a Shared ClusterMedium

  Design a job scheduler for machine learning workloads such as training runs and batch inference on a shared compute cluster with GPUs. It tests requirements gathering, queueing and fairness between teams, gang placement for distributed jobs, preemption and checkpointing, and recovery from node and scheduler failures.

  NetflixSoftware EngineerSystem DesignTechnical Screen
• Design a Scheduler for ML Training and Batch Inference JobsMedium

  This question tests the ability to design a distributed job scheduler capable of handling heterogeneous ML workloads with varying priority, resource requirements, and fault-tolerance constraints.

  NetflixSoftware EngineerSystem DesignOnsite
• Design a Streaming Subscription System: Billing, Plan Changes and Payment RetriesMedium

  Design the subscription backend of a video streaming service such as Netflix, covering the subscription lifecycle, recurring billing through an external payment provider, mid-cycle upgrades and downgrades, retries of failed charges, and scaling. It tests data modeling, safe payment handling, concurrency between member actions and billing jobs, and keeping entitlement checks fast.

  NetflixSoftware EngineerSystem DesignOther
• Design a crash-resilient file systemEasy

  This question evaluates understanding of durable storage and crash recovery mechanisms in file system design, including metadata consistency, durability guarantees, and operational concerns like performance and space overhead.

  NetflixSoftware EngineerSystem DesignTechnical Screen
• Design ad frequency cappingMedium

  This question evaluates a candidate's competence in large-scale system design, including distributed systems, real-time analytics, data modeling, and consistency and scalability trade-offs for ad frequency capping.

  NetflixSoftware EngineerSystem DesignOnsite
• Design ads frequency capping serviceHard

  This question evaluates a candidate's ability to design a low-latency, high-QPS, multi-tenant frequency capping service, testing competencies in identity resolution, hierarchical cap modeling, window semantics, counter and storage architecture, consistency versus latency trade-offs, real-time decisioning APIs, configuration/versioning, backfill, reporting, privacy/compliance, and failure handling. It is commonly asked to assess systems-design proficiency in distributed systems, databases, caching, API design, and operational reliability; it belongs to the System Design domain and emphasizes practical application with system-level conceptual reasoning rather than purely algorithmic detail.

  NetflixSoftware EngineerSystem DesignOnsite
• Design an Ad Pacing SystemHard

  This question evaluates a candidate's skill in designing large-scale, low-latency distributed advertising systems, including pacing algorithms, budgeting and spend tracking, concurrency control, event processing, and observability.

  NetflixSoftware EngineerSystem DesignOnsite
• Design an ad frequency capping systemHard

  This question evaluates competency in distributed systems architecture, real-time stateful services, high-cardinality time-series data modeling, API design for low-latency decision paths, and operational monitoring including consistency and latency trade-offs.

  NetflixSoftware EngineerSystem DesignOnsite
• Design an ads audience targeting systemHard

  This problem evaluates your ability to design a high-scale, low-latency system for filtering user profiles against complex, dynamic advertising audience segments.

  NetflixSoftware EngineerSystem DesignTechnical Screen
• Design an ads data modelMedium

  This problem tests your ability to design a scalable, reliable data schema for an advertising platform that must support high-throughput event ingestion and real-time analytical queries.

  NetflixSoftware EngineerSystem DesignOnsite
• Design config rollout and click aggregationHard

  This problem tests your ability to design a global configuration distribution system combined with a high-throughput event processing pipeline to monitor rollout health.

  NetflixData EngineerSystem DesignOnsite
• Design homepage viewport rendering with deduplicationMedium

  This problem tests the ability to design a content-delivery service that optimizes viewport rendering through client-side requests, server-side deduplication, and efficient data retrieval patterns.

  NetflixSoftware EngineerSystem DesignTechnical Screen
• Design viewport dedup for Netflix home pageHard

  Design viewport dedup for Netflix home page 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.

  NetflixSoftware EngineerSystem DesignTechnical Screen
• Model ads demand data for reportingMedium

  This question tests the ability to design a robust data pipeline and schema for tracking ad performance metrics, focusing on handling event-level data, aggregations, and dimension consistency.

  NetflixSoftware EngineerSystem DesignOnsite
• Model data for an ads platformMedium

  This question evaluates proficiency in data modeling and database schema design for advertising systems, including entity-relationship modeling, privacy-aware user identifier handling, and delivery/tracking log representation.

  NetflixSoftware EngineerSystem DesignOnsite
• Design a Kafka Consumer Service That Groups Trace Events by Trace IDMedium

  Design a distributed service that consumes trace events from Kafka, groups every event sharing a trace ID, and hands each complete trace to an existing processing function. Covers partitioning, in-memory versus external state, deciding when a trace is complete, and traces that stay open for hours.

  New RelicBackend EngineerSystem DesignOnsite
• Describe CAP, distributed systems, multithreading experienceHard

  This question evaluates practical and theoretical competence in the CAP theorem, distributed system architecture, and multithreaded concurrency, covering consistency models, partition tolerance, fault tolerance, scaling strategies, synchronization primitives, and debugging/performance techniques.

  NextdoorSoftware EngineerSystem DesignHR Screen
• Design KPI dashboard tables: retention, weekly rollups, timezoneHard

  This Nextdoor data engineer system design screen asks you to design the analytical data model and incremental ETL behind a fast, large-scale KPI dashboard for a photo-sharing app. It covers derived aggregate table design (grain, keys, partitioning), DAU/new-user/new-photo and follower-distribution metrics, 7-day deltas, then extends to week-1 cohort retention, weekly rollups, and switching the reporting timezone. The focus is correctness, performance at scale, and maintainability with timezone handled as a reporting parameter.

  NextdoorData EngineerSystem DesignTechnical Screen
• Design a Job Scheduler That Can Scale by 100 TimesMedium

  Design a distributed, asynchronous job scheduler that starts at modest traffic but can seamlessly scale up 100x. Evaluates API design, data storage trade-offs, worker coordination, task deduplication, and failure handling across scale boundaries.

  NextdoorSoftware EngineerSystem DesignTechnical Screen
• Design a Neighborhood Newsfeed SystemMedium

  Design a privacy-aware, hyperlocal social feed that serves real-time updates and emergency alerts based on strict neighborhood boundaries.

  NextdoorSoftware EngineerSystem DesignTechnical Screen
• Design a Scalable Job SchedulerMedium

  This question evaluates system design skills focused on job scheduling, distributed systems concerns (state modeling, retries, idempotency), API and data-model design, fault tolerance, observability, and scalability within the System Design domain.

  NextdoorSoftware EngineerSystem DesignTechnical Screen
• Design a URL Shortening Service: Code Generation, Storage and Fast RedirectsMedium

  A system design question on building a URL shortening service that turns long links into short codes and redirects visitors quickly at large scale. It tests requirement scoping, capacity estimates, collision-free code generation across servers, storage and caching choices, and how the redirect path behaves under failure.

  NielsenSoftware EngineerSystem DesignOther
• How would you throttle a crawler?Medium

  This question evaluates competency in scalable distributed system design, including rate limiting, request scheduling, concurrency control, fault tolerance, backoff/retry strategies, deduplication, and observability for networked crawlers.

  NooksSoftware EngineerSystem DesignOnsite
• Design a Shared Calendar ServiceMedium

  This problem tests the ability to design a scalable shared calendar system with support for recurring events, granular permission models, and notification scheduling.

  NotionSoftware EngineerSystem DesignOnsite