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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 511–540 of 9403 results
- AmazonMediumSoftware EngineerDesign a multi-size storage locker system
This question tests your ability to design a scalable, distributed reservation system that handles resource allocation (bin packing) under concurrency constraints.
System DesignOnsiteConcurrencyAPI DesignDistributed Systems - AmazonMediumSoftware EngineerDesign a no-repeat top-frequency music player
Design a no-repeat frequency-based music player with addUserSongs and getNextSong APIs. Covers max-heap data structures, lazy deletion, cycle resets, mid-cycle song additions, and complexity.
Coding & AlgorithmsOnsite - AmazonMediumSoftware EngineerDesign a package installer with dependencies
This question tests your ability to design a system that manages complex dependency graphs, ensuring package integrity, version compatibility, and atomic installation operations.
System DesignTechnical ScreenGraphsDependency ResolutionTransactions - AmazonMediumSoftware EngineerDesign a pizza ordering system
This question evaluates object-oriented design and software architecture skills, focusing on class modeling, responsibilities, relationships, state management, pricing, payment flows, and extensibility.
Software Engineering FundamentalsTechnical Screen - AmazonHardData ScientistDesign a robust traffic forecasting pipeline
This question tests the ability to design an end-to-end, scalable time-series forecasting pipeline, focusing on anomaly handling, structural decomposition, uncertainty quantification, and model validation strategies.
Machine LearningTechnical ScreenTime-SeriesForecastingState-Space Models - AmazonMediumSoftware EngineerDesign a scalable image-based social network
This question tests your ability to design a distributed system architecture capable of handling high-volume image uploads, metadata storage, and low-latency feed retrieval.
System DesignOnsiteScalabilityDistributed SystemsCaching - AmazonHardSoftware EngineerDesign a scalable parking lot system
Design a scalable parking lot system 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 - AmazonMediumSoftware EngineerDesign a weighted random value generator
This problem tests your ability to implement efficient weighted random selection using prefix sums and binary search, and handling state updates.
Coding & AlgorithmsTechnical ScreenBinary SearchHash MapProbability - AmazonMediumData ScientistDesign an A/B Test for Dashboard Engagement Impact
Amazon data scientist experimentation prompt on designing an A/B test for an energy-usage dashboard redesign, including hypotheses, metrics, guardrails, power, test duration, seasonality, and analysis methods.
Analytics & ExperimentationOnsite - AmazonMediumSoftware EngineerDesign an AI Fraud-Triage Agent Over a 1 TB/Day Payments Event Stream
Design a service where an AI agent investigates flagged payment events inside a stream running at roughly 1 TB of events per day, covering capacity math, where the agent sits relative to deterministic detection, and the cost and adversarial-input guardrails that keep it from becoming a liability.
System DesignTechnical Screensystem-designstreamingfraud - AmazonEasySoftware EngineerDesign an Amazon Locker Service
Tests object-oriented design and state-machine modeling for a self-service package locker station, focusing on parcel allocation, code expiration, and physical-vs-logical state consistency.
System DesignTechnical ScreenObject-Oriented DesignState MachineIoT - AmazonMediumData ScientistDesign an Automated Home-Price Valuation Model
Design an Automated Home-Price Valuation Model evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - AmazonMediumSoftware EngineerDesign an Extensible Cinema Ticket Pricing Engine
Design an extensible cinema pricing engine with validated money and multiplier types, composable policies, explicit calculation and rounding, compatibility checks, versioned configuration, itemized quotes, and boundary tests.
Software Engineering FundamentalsTechnical Screen - AmazonMediumSoftware EngineerDesign an In-Memory Pub-Sub Model
This question evaluates object-oriented design and API modeling skills, knowledge of messaging/pub-sub semantics, concurrency and thread-safety, and the ability to reason about extensibility for retries, persistence, and asynchronous delivery.
Software Engineering FundamentalsTechnical Screen - AmazonMediumApplied ScientistDesign an LLM quality validation system
This question evaluates an engineer's competency in ML system design for validating large language models, testing knowledge of evaluation metrics, validation scopes from internal numerics to final responses, system architecture for automated validation pipelines, and operational concerns within the ML System Design domain; it requires both conceptual architecture understanding and practical application-level thinking. It is commonly asked to assess the ability to reason about correctness, safety, regression risk, scalability, reproducibility, and monitoring trade-offs when deploying production-grade validation pipelines and to gauge familiarity with complementary evaluation methods and end-to-end data flows.
ML System DesignOnsite - AmazonHardData ScientistDesign an ML Model for Interview Recommendation Pipeline
Design an ML Model for Interview Recommendation Pipeline evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - AmazonMediumSoftware EngineerDesign an Object-Oriented Shopping Cart
Tests object-oriented design principles, domain modeling for monetary precision, extensible discount strategies, and thread-safe state mutations.
Software Engineering FundamentalsTechnical ScreenObject-Oriented DesignDesign PatternsConcurrency - AmazonMediumApplied ScientistDesign an S3-like object storage service
This question evaluates understanding of distributed systems, object storage architecture, REST API design, multipart upload semantics, replication and durability strategies, failure recovery, and scalability concerns.
System DesignOnsite - AmazonMediumApplied ScientistDesign an advertiser metrics tracking platform
This question tests your ability to design a flexible, object-oriented system for ingesting, processing, and querying real-time advertising performance metrics.
Software Engineering FundamentalsTechnical ScreenSystem DesignOOPDesign Patterns - AmazonHardSoftware EngineerDesign an e-commerce recommendation system
This question evaluates expertise in large-scale machine learning system design, covering competencies in recommendation algorithms, candidate generation and ranking, feature engineering and stores, low-latency serving, and operational reliability; it is categorized in the ML system design domain and requires both conceptual understanding and practical application. It is commonly asked to assess the ability to balance personalization and business metrics (CTR, CVR, revenue), address scalability and latency targets, and manage data quality, cold-start, exploration–exploitation, A/B testing, bias/fairness, and monitoring trade-offs in production.
ML System DesignTechnical Screen - AmazonHardSoftware EngineerDesign an email spam detection system
This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design an email spam detection system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - AmazonHardData ScientistDesign an end-to-end spam detection system
This question evaluates a data scientist's system-design and applied machine learning engineering skills—covering problem framing and labeling, feature representation, model selection and calibration, real-time serving constraints, drift detection, and feedback/safety mechanisms—and is commonly asked to probe trade-offs between latency, precision/recall, and robustness against adversarial evolution in production spam detection. Category: Machine Learning; it tests machine learning systems and production-ML competencies at both conceptual-design and practical-application levels, emphasizing calibration, evaluation (offline and online), operational reliability, and rollback/mitigation planning.
Machine LearningTechnical Screen - AmazonHardSoftware EngineerDesign an extensible ticketing system
This question evaluates software architecture and system-design competencies, covering extensibility patterns, concurrency control for high-throughput ticketing, transactional and idempotency guarantees, persistence/schema modeling, API design, and pluggable pricing strategies.
System DesignTechnical Screen - AmazonHardSoftware EngineerDesign an intersection traffic-light system
This question evaluates system design competency across real-time control and safety-critical logic, API and data modeling, sensor/actuator integration, and operational scalability for traffic-signal controllers.
System DesignTechnical Screen - AmazonHardSoftware EngineerDesign an online bookstore for browse and purchase
Design an online bookstore for browse and purchase 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 - AmazonHardData ScientistDesign an operations dashboard with justifications
This question evaluates a data scientist's ability to define precise operational metrics, justify visualization choices, design interactive drill-downs and alerting, and architect aggregation strategies for high-volume same-day delivery events.
Analytics & ExperimentationOnsite - AmazonMediumSoftware EngineerDesign and Implement a Shopping Cart Object Model for an Online Store
An object-oriented design exercise that asks you to model and implement a shopping cart for an online store, covering adding, updating and removing products and computing the total. It tests class boundaries, explicit edge-case policies, exact money handling and how cleanly the design extends to promotions.
Software Engineering FundamentalsOnsite - AmazonHardData ScientistDesign and analyze pricing-page A/B test
This question evaluates data-science competencies in online experiment design and analysis, including statistical power and sample-size estimation, variance-reduction methods, guardrail and SRM monitoring, randomization and contamination handling, sequential monitoring and stopping rules, heterogeneity/subgroup analysis, and result communication within the analytics & experimentation domain. It is commonly asked to assess practical application of statistical inference and experimental rigor in product and business contexts, primarily testing practical application while requiring conceptual understanding of underlying statistical principles and operational trade-offs.
Analytics & ExperimentationOnsite - AmazonEasyData ScientistDesign and evaluate a RAG system
This question evaluates a candidate's competency in designing and evaluating retrieval-augmented generation (RAG) systems, including document ingestion, chunking, embedding and retrieval strategies, reranking, prompt construction, grounding/citation, operational constraints like latency and freshness, permission handling, and evaluation metrics and failure modes. It is commonly asked to assess practical system-design and applied machine learning skills for LLM applications, testing knowledge in the Machine Learning/Information Retrieval domain and requiring both practical application-level reasoning about trade-offs (latency, cost, precision vs. recall) and conceptual understanding of evaluation and guardrails.
Machine LearningTechnical Screen - AmazonHardData ScientistDesign causal study for reminder impact
This question evaluates a data scientist's competency in observational causal inference and program evaluation, covering concepts such as staggered rollouts, treatment definition, difference-in-differences diagnostics, matching/weighting, negative controls, interference handling, and power/MDE planning.
Analytics & ExperimentationHR Screen
Amazon