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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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- 546
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Showing 451–480 of 9403 results
- AmazonEasyApplied ScientistDescribe overfitting and L1/L2 regularization
This question evaluates understanding of model generalization and regularization techniques by probing knowledge of overfitting and L1/L2 regularization and their effects on model parameters.
Machine LearningTechnical Screen - AmazonMediumData EngineerDescribe past NLP work and collaboration
This question evaluates a candidate's technical expertise in applied NLP methods, data engineering competencies, and collaborative leadership in managing annotation workflows, along with their ability to articulate specific contributions, trade-offs, and metrics from past projects.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDescribe projects and handle challenges
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe projects and handle challenges states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - AmazonEasyProduct ManagerDescribe using customer data
Describe using customer data evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDesign 2D DP on a DAG
Design 2D DP on a DAG evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - AmazonHardData ScientistDesign A/B Test for New Amazon Recommendation Module
Design A/B Test for New Amazon Recommendation Module evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - AmazonMediumSoftware EngineerDesign Faceted Product Search at Large Scale
Design a scalable e-commerce product search engine supporting full-text query matching, multi-attribute filtering, dynamic facet aggregations, and near real-time updates for volatile data like price and inventory.
System DesignOnsiteSearchDistributed SystemsInformation Retrieval - AmazonMediumSoftware EngineerDesign Human Avoidance for Warehouse Robots
This problem evaluates your ability to design a real-time sensor-to-actuator pipeline for an autonomous robot, focusing on latency, sensor fusion, and safety-critical path adjustments.
System DesignTechnical ScreenRoboticsSensor FusionReal-time Systems - AmazonMediumData ScientistDesign Incremental Load Process for Large Relational Table
This question evaluates understanding of incremental loading, change-data-capture, idempotent upsert logic, deduplication, partitioning, and data quality controls for large relational tables within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumSoftware EngineerDesign LCA with flexible inputs and rule
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Design LCA with flexible inputs and rule states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - AmazonMediumApplied ScientistDesign LFU cache with distributed extension
This question evaluates understanding of cache design and frequency-based eviction policies, efficient data structures that support O(1) operations, and techniques for approximate frequency estimation and summarization in distributed streaming environments.
Coding & AlgorithmsOnsite - AmazonMediumSoftware EngineerDesign Music Search, Playlists, and Playback Controls
This problem tests the ability to design a scalable music streaming architecture, focusing on data modeling for search indices, conflict resolution in distributed state management, and robust API design for real-time playback control.
System DesignOnsiteDistributed SystemsAPIsConcurrency - AmazonMediumSoftware EngineerDesign Sent, Delivered and Read Receipts for a Mobile Messaging App
A system design question about the sent, delivered and read indicators in a large mobile messaging app, for one-to-one and group chats. It tests how status updates travel from the recipient's device back to the sender, offline and multi-device behaviour, keeping status monotonic, and controlling the volume of status events.
System DesignOther - AmazonHardSoftware EngineerDesign Thread-Safe Tagging and Bookstore Systems
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 Thread-Safe Tagging and Bookstore Systems states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - AmazonHardSoftware EngineerDesign VPC, IP plan, management, storage, SSO
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 VPC, IP plan, management, storage, SSO states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - AmazonMediumData ScientistDesign a Churn Model: Handle Missing Data and Justify
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Design a Churn Model: Handle Missing Data and Justify states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - AmazonHardSoftware EngineerDesign a Core Dump Collection and Analysis System
This problem tests the ability to design a distributed, scalable ingestion and analysis pipeline for massive binary crash files while maintaining system performance and data availability.
System DesignOnsiteDistributed SystemsScalabilityStorage - AmazonMediumFrontend EngineerDesign a Debounced Product Search Box
Design and implement an accessible, resilient product search-as-you-type component featuring debounced requests, race-condition handling via request cancellation, client-side caching, and keyboard navigation.
Software Engineering FundamentalsOnsitefrontendjavascriptreact - AmazonMediumSoftware EngineerDesign a Distributed Cron Job Scheduler
Design a fault-tolerant, horizontally scalable distributed cron scheduler capable of triggering millions of recurring tasks with at-least-once delivery semantics and deterministic run coordination.
System DesignOnsiteDistributed SystemsSchedulingFault Tolerance - AmazonMediumSoftware EngineerDesign a Distributed In-Memory Cache: Partitioning, Failover and Invalidation
Design a distributed in-memory cache shared by many services in front of a database, covering single-node eviction and expiry, key partitioning and rebalancing, replication and failover, and keeping cached values consistent after writes. It also probes protection against miss stampedes and hot keys.
System DesignOther - AmazonHardSoftware EngineerDesign a Distributed Job Scheduler with Run Logs and Exactly-Once Execution
Design a highly available distributed job scheduler that handles one-time and recurring jobs, collects execution logs, and rigorously addresses the practical boundaries of exactly-once execution.
System DesignOnsitesystem-designdistributed-systemsconcurrency - AmazonHardSoftware EngineerDesign a High-Availability Load Balancer
This problem tests the ability to architect a highly available, scalable Layer 7 load balancer, focusing on fault tolerance, traffic distribution strategies, and system reliability.
System DesignOnsiteDistributed SystemsLoad BalancingHigh Availability - AmazonMediumSoftware EngineerDesign a Kafka-Based File Watcher for Millions of Files
Design a Kafka file watcher for millions of files with source sharding, durable events, per-file ordering, missed-event reconciliation, and version-aware reads.
System DesignTechnical Screen - AmazonHardSoftware EngineerDesign a Library Management System (API and Schema)
This problem evaluates your ability to design a schema and API for a transactional library system, focusing on handling concurrent checkouts and maintaining data integrity in a read-heavy environment.
System DesignTechnical ScreenDatabase ModelingConcurrencyREST API - AmazonMediumSoftware EngineerDesign a Linked Queue with Deletion Follow-Ups
Implement a linked-list-based FIFO queue supporting constant-time core operations, extended with value-based deletion methods that correctly maintain queue integrity and pointer boundaries.
Software Engineering FundamentalsOnsitelinked-listqueuedata-structures - AmazonMediumSoftware EngineerDesign a Low-Latency Ad Serving Backend With Targeting, Budgets and Tracking
An Amazon system design question: design an ad server that selects and returns an ad for each page request within a tight latency budget. It covers targeting and candidate retrieval, ranking and auctions, budget enforcement and pacing, impression and click tracking, and failure handling.
System DesignOnsite - AmazonHardData ScientistDesign a Machine Learning Recommendation System Pipeline
Design a Machine Learning Recommendation System 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 a Multi-Branch Library Lending System
This problem tests the ability to design a distributed inventory and reservation system, focusing on concurrency control, transaction integrity across branches, and fault-tolerant service communication.
System DesignTechnical ScreenConcurrencyDistributed SystemsInventory - AmazonMediumSoftware EngineerDesign a Multi-Provider Financial Dashboard
Design a financial dashboard that reconciles balances, securities, cryptocurrency, prices, and derived metrics from unreliable external providers. Explain freshness, historical reproducibility, permissions, corrections, outages, and how users can recognize partial or inconsistent data.
System DesignOnsite - AmazonHardApplied ScientistDesign a Multimodal Neural Network
This question evaluates a candidate's competency in designing production-grade multimodal machine learning systems, including architecture choices for text and image encoders, cross-modal fusion strategies, training objectives for joint retrieval and classification, robustness to missing or noisy modalities, and considerations for scalability and low-latency serving. Commonly asked in ML system design interviews to assess both conceptual understanding and practical application of machine learning engineering principles, it falls under the ML System Design domain and probes abilities in model alignment, evaluation metrics, deployment trade-offs, and domain adaptation strategies.
ML System DesignOnsite
Amazon