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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.
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Showing 5911–5940 of 9403 results
- NetflixMediumSoftware EngineerDesign Rolling-Window Ad Frequency Capping
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.
System DesignOnsite - NetflixMediumSoftware EngineerDesign a Distributed Frequency-Capping Service
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.
System DesignOnsiteDistributed SystemsCachingAdTech - NetflixHardSoftware EngineerDesign a File Backup System
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.
System DesignTechnical Screen - NetflixMediumSoftware EngineerDesign a Job Scheduler for ML Training and Batch Inference on a Shared Cluster
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.
System DesignTechnical Screen - NetflixMediumSoftware EngineerDesign a Scheduler for ML Training and Batch Inference Jobs
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.
System DesignOnsiteDistributed SystemsSchedulingResource Management - NetflixMediumSoftware EngineerDesign a Streaming Subscription System: Billing, Plan Changes and Payment Retries
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.
System DesignOther - NetflixHardSoftware EngineerDesign a concurrent latency percentile tracker
This problem tests your ability to design a high-throughput, thread-safe data structure capable of tracking request latencies and calculating arbitrary percentiles (e.g., p99) within a sliding time window.
Software Engineering FundamentalsTechnical ScreenConcurrencyData StructuresMetrics - NetflixEasySoftware EngineerDesign a crash-resilient file system
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.
System DesignTechnical Screen - NetflixHardData ScientistDesign a robust conversion propensity model
This question evaluates a candidate's competency in end-to-end propensity modeling, covering correct labeling to avoid post-treatment leakage, time-windowed feature engineering, class-imbalance and calibration strategies, causal uplift estimation, and production concerns such as monitoring, serving consistency, and cold-start scoring.
Machine LearningOnsite - NetflixMediumSoftware EngineerDesign a thread-safe key-value store
This question evaluates knowledge of concurrent programming and thread-safety, including the choice of in-memory data structures for a key–value store and synchronization techniques that ensure atomic put/get/delete operations without internal corruption.
Software Engineering FundamentalsTechnical Screen - NetflixMediumSoftware EngineerDesign ad frequency capping
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.
System DesignOnsite - NetflixHardSoftware EngineerDesign ads frequency capping service
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.
System DesignOnsite - NetflixHardSoftware EngineerDesign an Ad Pacing System
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.
System DesignOnsite - NetflixEasyMachine Learning EngineerDesign an ML Platform Portal
This question assesses the ability to design an internal ML Platform Portal that streamlines the model lifecycle, focusing on experiment tracking, model registry, and workflow automation.
ML System DesignOnsiteMLOpsSystem DesignScalability - NetflixHardSoftware EngineerDesign an ad frequency capping system
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.
System DesignOnsite - NetflixHardSoftware EngineerDesign an ads audience targeting system
This problem evaluates your ability to design a high-scale, low-latency system for filtering user profiles against complex, dynamic advertising audience segments.
System DesignTechnical ScreenScalabilityDistributed SystemsCaching - NetflixMediumSoftware EngineerDesign an ads data model
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.
System DesignOnsiteData ModelingScalabilityOLTP vs OLAP - NetflixHardSoftware EngineerDesign command executor with undo
This problem tests the application of the Command design pattern and stack-based data structures to manage reversible operations in an application state.
Coding & AlgorithmsTechnical ScreenDesign PatternsStacksCommand Pattern - NetflixHardData EngineerDesign config rollout and click aggregation
This problem tests your ability to design a global configuration distribution system combined with a high-throughput event processing pipeline to monitor rollout health.
System DesignOnsiteDistributed SystemsReal-time AnalyticsEvent Streaming - NetflixMediumData ScientistDesign experiment on culture memo emphasis
This question tests the ability to design a rigorous A/B test for a qualitative product change, focusing on metric selection, potential bias mitigation, and pre-experimental planning.
Analytics & ExperimentationHR ScreenA/B TestingExperiment DesignMetrics - NetflixMediumSoftware EngineerDesign homepage viewport rendering with deduplication
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.
System DesignTechnical ScreenAPI DesignCachingHigh Availability - NetflixHardSoftware EngineerDesign viewport dedup for Netflix home page
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.
System DesignTechnical Screen - NetflixMediumData ScientistDetermine Maximum Consecutive Order Days Per User
Determine Maximum Consecutive Order Days Per User evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Data Manipulation (SQL/Python)Onsite - NetflixMediumData ScientistEstimate ATE of personalization on streaming
This question evaluates causal inference and experimental-analysis competencies by asking for estimation of the average treatment effect (ATE) of personalization on minutes streamed, reporting a 95% confidence interval, and reasoning about the use of pre-treatment covariates, and it sits in the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to assess application of randomized experiment analysis and statistical inference while probing conceptual understanding of causal assumptions and covariate adjustment, testing both conceptual understanding and practical application.
Analytics & ExperimentationOnsite - NetflixEasyData ScientistEstimate ATE, ITT, and TOT from experiment
This question tests the ability to distinguish and calculate Average Treatment Effect (ATE), Intent-to-Treat (ITT), and Treatment-on-the-Treated (TOT) in the presence of non-compliance.
Analytics & ExperimentationOnsiteCausal InferenceExperimentationInstrumental Variables - NetflixHardData ScientistEvaluate Propensity Score Matching Alternatives and Diagnostics
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Evaluate Propensity Score Matching Alternatives and Diagnostics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOther - NetflixHardData EngineerExplain concurrency and reliability tradeoffs
This problem tests your understanding of balancing thread-safe resource management in a high-concurrency local environment against the systemic requirements of a distributed data pipeline.
Software Engineering FundamentalsOnsiteConcurrencyDistributed SystemsCaching - NetflixMediumMachine Learning EngineerExplain self-attention, LoRA, Adam vs SGD, ViT
This question evaluates understanding of modern Machine Learning/Deep Learning topics, including self-attention mechanics (queries, keys, values and scaled logits), Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning and memory savings, optimizer behavior (Adam versus SGD with momentum), and architectural trade-offs between Vision Transformers and CNNs including patch-size considerations. It is categorized under Machine Learning and is commonly asked because it probes both conceptual understanding and practical application—testing reasoning about training dynamics, model scaling, fine-tuning strategies, and resource/performance trade-offs.
Machine LearningTechnical Screen - NetflixMediumMachine Learning EngineerExplain tokenization and Transformer variants
This question evaluates understanding of tokenization techniques and Transformer architecture, covering competencies in subword and SentencePiece-style tokenizers, Transformer block internals, and comparisons of modern architectural variants and trade-offs.
Machine LearningTechnical Screen - NetflixMediumSoftware EngineerFind longest run of identical consecutive shows
This question tests the ability to track state while iterating through an array to identify the longest contiguous sub-sequence of identical elements.
Coding & AlgorithmsTechnical ScreenArraysIterationAlgorithms
Netflix