Amazon interview questions

Showing 241–270 of 653 results

• Design an In-Memory Pub-Sub ModelMedium

  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.

  AmazonSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design an LLM quality validation systemMedium

  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.

  AmazonApplied ScientistML System DesignOnsite
• Design an ML Model for Interview Recommendation PipelineHard

  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.

  AmazonData ScientistMachine LearningOnsite
• Design an Object-Oriented Shopping CartMedium

  Tests object-oriented design principles, domain modeling for monetary precision, extensible discount strategies, and thread-safe state mutations.

  AmazonSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design an S3-like object storage serviceMedium

  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.

  AmazonApplied ScientistSystem DesignOnsite
• Design an advertiser metrics tracking platformMedium

  This question tests your ability to design a flexible, object-oriented system for ingesting, processing, and querying real-time advertising performance metrics.

  AmazonApplied ScientistSoftware Engineering FundamentalsTechnical Screen
• Design an e-commerce recommendation systemHard

  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.

  AmazonSoftware EngineerML System DesignTechnical Screen
• Design an email spam detection systemHard

  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.

  AmazonSoftware EngineerML System DesignTechnical Screen
• Design an end-to-end spam detection systemHard

  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.

  AmazonData ScientistMachine LearningTechnical Screen
• Design an extensible ticketing systemHard

  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.

  AmazonSoftware EngineerSystem DesignTechnical Screen
• Design an intersection traffic-light systemHard

  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.

  AmazonSoftware EngineerSystem DesignTechnical Screen
• Design an online bookstore for browse and purchaseHard

  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.

  AmazonSoftware EngineerSystem DesignOnsite
• Design an operations dashboard with justificationsHard

  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.

  AmazonData ScientistAnalytics & ExperimentationOnsite
• Design and Implement a Shopping Cart Object Model for an Online StoreMedium

  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.

  AmazonSoftware EngineerSoftware Engineering FundamentalsOnsite
• Design and analyze pricing-page A/B testHard

  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.

  AmazonData ScientistAnalytics & ExperimentationOnsite
• Design and evaluate a RAG systemEasy

  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.

  AmazonData ScientistMachine LearningTechnical Screen
• Design causal study for reminder impactHard

  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.

  AmazonData ScientistAnalytics & ExperimentationHR Screen
• Design delayed job scheduler (LLD)Hard

  This question evaluates system design and distributed-systems competencies, including scheduling algorithms, durable persistence and recovery, concurrency control, delivery semantics, and operational concerns, and it belongs to the System Design category.

  AmazonSoftware EngineerSystem DesignOnsite
• Design document-tag classes with concurrency safetyHard

  Design document-tag classes with concurrency safety 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.

  AmazonSoftware EngineerSystem DesignOnsite
• Design end-to-end regression for energy demandHard

  This question evaluates a candidate's ability to design and justify an end-to-end regression system for next-day site-level energy demand forecasting, testing competencies in time-series modeling, feature engineering, regularization, evaluation metrics and SLAs, production ML pipelines, monitoring, explainability, and data privacy.

  AmazonData ScientistMachine LearningOnsite
• Design faster delay-time computationMedium

  This question evaluates a candidate's ability to design efficient array algorithms and appropriate data structures for nearest-smaller-element queries, assessing performance, correctness with duplicates, and scalability to large inputs.

  AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
• Design fraud detection across channels with unknownsHard

  This question tests the ability to design an end-to-end fraud detection system that accounts for cold-start problems, cross-channel signals, and the trade-off between customer friction and loss mitigation.

  AmazonData ScientistMachine LearningOnsite
• Design globally consistent metadata serviceMedium

  This question tests the ability to design a globally distributed metadata service that requires strict consistency for frequent read/write operations across multiple geographic regions.

  AmazonSoftware EngineerSystem DesignOnsite
• Design in-memory threshold switcherHard

  This question evaluates a candidate's ability to design memory-aware control systems, covering skills such as hysteresis-based thresholding, dynamic runtime reconfiguration, thread-safe concurrency, low-overhead metric sampling, event-driven state notifications, and handling multi-process and failure edge cases.

  AmazonSoftware EngineerSystem DesignTechnical Screen
• Design locker allocation serviceHard

  This question evaluates a candidate's understanding of stateful system design, resource allocation strategies, concurrency control, API and data modeling, and fault tolerance in the context of real-time locker assignment.

  AmazonSoftware EngineerSystem DesignTechnical Screen
• Design multi-tenant ingestion and processing platformMedium

  This question tests the ability to design a multi-tenant data platform that ensures resource fairness, performance isolation, and cost attribution at scale.

  AmazonSoftware EngineerSystem DesignOnsite
• Design object model for an elevator systemMedium

  This question evaluates object-oriented design and system-architecture skills for modeling a multi-elevator control system, including class responsibilities, interactions, request flow, and scheduling policies.

  AmazonSoftware EngineerSoftware Engineering FundamentalsOnsite
• Design pickup point planning for ride-hailingMedium

  Design a ride-hailing pickup-point planning system. Covers map snapping, candidate generation, driver and passenger routing, safety filtering, scoring objectives, app presentation, architecture, historical learning, and A/B metrics.

  AmazonSoftware EngineerSystem DesignTechnical Screen
• Design real-time top-K products serviceHard

  An Amazon software-engineer system-design screen: design a real-time service that ingests a stream of purchase events (customer_id, item_id, timestamp) and continuously serves the top-K most purchased items over multiple rolling windows (e.g. last 5 minutes, last 1 hour, 24 hours, and daily totals). The answer covers ingestion and partitioning, event-time windowing with watermarks, exact min-heap vs. approximate Count-Min-Sketch / Space-Saving top-K, late/duplicate/out-of-order handling, state and TTL, exactly-once fault tolerance, horizontal scaling, serving APIs, and latency-vs-accuracy trade-offs.

  AmazonSoftware EngineerSystem DesignTechnical Screen
• Design ride-hailing pricing with fairness and poolingHard

  This question evaluates system design and algorithmic skills related to real-time distributed pricing, API and data-flow architecture, carpooling logic, fairness constraints, and experimentation support.

  AmazonSoftware EngineerSystem DesignOnsite