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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 211–240 of 4616 results
- AmazonEasySoftware EngineerDescribe a failure and what you learned
This question evaluates self-awareness, accountability, learning agility, and leadership by asking the candidate to describe a past failure and the lessons derived from it.
Behavioral & LeadershipTechnical Screen - AmazonEasyData ScientistDescribe a time you solved a complex problem
This question evaluates analytical problem-solving, investigative data-analysis, data validation, and leadership/ownership competencies in the Behavioral & Leadership domain for a Data Scientist role.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDescribe a tough trade-off decision
This question evaluates decision-making under time pressure, trade-off analysis, stakeholder alignment, risk communication, and impact measurement in a Software Engineer context.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDescribe achievements, ambiguity, conflict, efficiency, challenges
Describe achievements, ambiguity, conflict, efficiency, challenges 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 EngineerDescribe challenges, mistakes, timeline management, communication
This question evaluates leadership, accountability, timeline and risk management, stakeholder communication, and incident-handling competencies for a software engineer and is categorized under Behavioral & Leadership.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDescribe deadline pressure, challenges, and decision changes
Describe deadline pressure, challenges, and decision changes 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 EngineerDescribe diving deep into a problem
Describe diving deep into a problem 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 EngineerDescribe failure, conflict, metrics, and AI lessons
This question evaluates behavioral and leadership competencies such as ownership of failures and successes, conflict resolution with teammates and stakeholders, metrics-driven impact assessment, communication clarity, and awareness of generative AI failure modes and safeguards.
Behavioral & LeadershipTechnical Screen - AmazonEasySoftware EngineerDescribe handling deadlines, pride, and conflict
This question evaluates time management, prioritization, accountability, stakeholder communication, and conflict-resolution competencies by probing past examples of meeting tight deadlines, describing a proud accomplishment, and mediating team disputes.
Behavioral & LeadershipTechnical Screen - 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 - 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 - 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 - 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 - 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 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 - AmazonHardBusiness Intelligence EngineerDesign a One-Page Warehouse Inbound Dashboard
Design an executive warehouse inbound dashboard with throughput, dock-to-stock time, aged backlog, capacity, and quality metrics.
Analytics & ExperimentationTechnical Screen - AmazonMediumSoftware EngineerDesign a Perishable-Goods Inventory and Location Tracking System
This question tests the ability to design a distributed inventory system that handles high-frequency location updates and strict FIFO/expiry logic for perishable goods.
System DesignTechnical ScreenGeo-spatialScalabilityDatabase Modeling - AmazonHardApplied ScientistDesign a RAG system end to end
This question evaluates a candidate's ability to design a production-grade Retrieval‑Augmented Generation (RAG) system, testing competencies in scalable ML system architecture, embedding and vector retrieval strategies, prompt orchestration, freshness and latency engineering, security/access controls, and evaluation metrics.
ML System DesignTechnical Screen - AmazonEasySoftware EngineerDesign a Reliable AI Chatbot Architecture
Tests candidate's ability to design an enterprise-grade Retrieval-Augmented Generation (RAG) chatbot system featuring fine-grained authorization, hallucination guardrails, and low-latency response serving.
ML System DesignTechnical ScreenRAGLLMInformation Retrieval - AmazonEasySoftware EngineerDesign a Scalable News Feed
This problem evaluates your ability to design a feed generation architecture, specifically comparing push vs. pull models for users with varying follower counts.
System DesignTechnical ScreenCachingScalabilityDatabases - AmazonMediumSoftware EngineerDesign a basic credit card system
This problem tests object-oriented modeling and state management by requiring the design of a class structure that handles credit limits, transaction authorization, and balance tracking.
Software Engineering FundamentalsTechnical ScreenObject-Oriented DesignDomain ModelingSystem Architecture - AmazonHardSoftware EngineerDesign a cloud database write path and recovery
This question tests the ability to architect a distributed database write path that decouples compute from storage, specifically focusing on how to maintain ACID properties during failures.
System DesignTechnical ScreenDistributed SystemsDatabasesDurability - AmazonHardSoftware EngineerDesign a fraud detection system
This problem evaluates your ability to design a scalable, low-latency fraud detection system for an e-commerce platform, focusing on handling class imbalance, feature engineering, and the challenges of delayed feedback loops.
ML System DesignTechnical ScreenFraud DetectionScalabilityFeature Store - AmazonMediumSoftware EngineerDesign a hierarchical locking tree
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 a hierarchical locking tree states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen
Amazon