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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 181–210 of 4616 results
- AmazonMediumData ScientistCreate Country-Level Spend Report Using Pandas
This question evaluates proficiency in data manipulation using pandas, including relational joins and aggregation to produce country-level summary statistics like totals and averages.
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumData ScientistCreate SQL Queries for Sales and Customer Analysis
This question evaluates proficiency in SQL-based data manipulation, covering aggregation and dispersion metrics, null handling, relational joins, and window functions for ranking customers.
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumSoftware EngineerCreate a Binary Tree from Parent-Child Descriptions
Construct one valid binary tree from unordered parent-child descriptions that identify left and right links. Reuse node identities across records, support children appearing before parents, and return the unique root.
Coding & AlgorithmsTechnical Screen - AmazonHardSoftware EngineerDebug an Issue-Creation API Failure
This problem tests your systematic troubleshooting methodology for a distributed system, focusing on narrowing down the root cause of an API request failure.
Software Engineering FundamentalsTechnical ScreenDebuggingSystem ArchitectureDistributed Systems - AmazonMediumData ScientistDecide standardization, sparse numerics, correlated features
This question evaluates a candidate's competency in feature engineering and preprocessing for mixed-type tabular data, including handling sparse counts and heavy-tailed monetary features, missingness and zero-inflation, correlated continuous measurements, model-specific scaling needs, and the design of leak-safe pipelines and validation strategies.
Machine LearningTechnical Screen - AmazonEasyBusiness Intelligence EngineerDefend an Experiment Decision and Its Incremental Impact
This question tests your ability to articulate a data-driven decision-making process, specifically focusing on the nuance of non-obvious outcomes and the business impact of choosing not to launch a feature.
Behavioral & LeadershipTechnical ScreenBehavioralA/B TestingStakeholder Management - AmazonMediumSoftware EngineerDeliver Under a Tight Deadline with Clear Tradeoffs
Explain a tight-deadline delivery through a concise situation, scope tradeoff, personal action, stakeholder agreement, and verifiable result.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistDeliver a Data Solution Under Tight Deadlines
Deliver a Data Solution Under Tight Deadlines 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 EngineerDeliver a Feature Under a Tight Deadline
This question assesses a candidate's ability to prioritize project scope, manage cross-functional dependencies, and maintain delivery quality under significant time pressure.
Behavioral & LeadershipTechnical ScreenProject ManagementPrioritizationLeadership - AmazonHardProduct ManagerDelivery Driver Performance Evaluation Framework
Practice designing a fair delivery-driver performance framework that goes beyond package count and total time. The solution covers route difficulty, weather, traffic, package attributes, vehicle data, safety gates, expected-versus-actual scoring, coaching dashboards, bias monitoring, and metric governance.
Product / Decision MakingTechnical Screen - AmazonMediumSoftware EngineerDemonstrate Bias for Action
Demonstrate Bias for Action 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 - AmazonMediumData ScientistDemonstrate Initiative Beyond Job Responsibilities
Evaluates ownership and curiosity beyond formal job responsibilities. Strong answers show a concrete initiative, explain what the candidate had to learn, describe stakeholder alignment and de-risking, quantify the impact, and reflect on how the experience changed their approach.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDemonstrate Leadership Principles in Projects
Demonstrate Leadership Principles in Projects 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 - AmazonEasySoftware EngineerDemonstrate Project Ownership and In-Depth Investigation
Tests your ability to demonstrate Amazon Leadership Principles—specifically Ownership and Dive Deep—by explaining how you diagnosed a complex technical problem and saw the solution through to completion.
Behavioral & LeadershipTechnical Screenbehavioralamazon-lpownership - AmazonMediumData ScientistDemonstrate calculated risk and deep-dive leadership
This question evaluates calculated risk-taking, initiative, deep-dive leadership, experimental design, risk management, and outcome quantification skills relevant to a Data Scientist role.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDemonstrate decision-making and collaboration behaviors
This question evaluates decision-making, collaboration, conflict resolution, feedback exchange, leadership, and situational judgment through concrete interpersonal scenarios such as ambiguity, pressure, and deadline-driven teamwork, and it tests competencies in the Behavioral & Leadership domain while emphasizing practical application rather than abstract theory. Interviewers commonly ask this type of prompt in technical screens to determine how a software engineer demonstrates real-world leadership and teamwork behaviors, quantifies outcomes, and applies judgment and communication skills under ambiguous or time-sensitive conditions.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistDemonstrate invent-and-simplify and customer communication
This question evaluates a candidate's competency in invent-and-simplify and difficult customer communication, emphasizing process redesign, trade-off analysis, risk mitigation, stakeholder persuasion, measurable outcomes, and clear written alignment.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistDemonstrate leadership with quantifiable STAR stories
This question evaluates leadership, ownership, and communication competencies for a Data Scientist by requiring concise, quantifiable STAR(L) narratives that capture role, team, stakeholders, constraints, actions, trade-offs, and measurable results.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDemonstrate ownership and deliver results
This question evaluates ownership, leadership, prioritization, stakeholder alignment, risk management, and the ability to deliver measurable results under tight deadlines and limited resources.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistDemonstrate ownership beyond responsibilities
This question evaluates ownership, proactive initiative, cross-functional leadership, stakeholder alignment, risk management, handling resistance, trade-off analysis, and the ability to quantify business impact within a Data Scientist role.
Behavioral & LeadershipTechnical Screen - AmazonHardData ScientistDerive and compare core ML and RL methods
This question evaluates a candidate's mastery of core machine learning and reinforcement learning concepts, including optimization (gradient methods and batch‑size trade‑offs), supervised versus unsupervised algorithms, policy‑gradient RL and variance reduction, deep RL stability techniques, sequence model complexity (Transformers vs RNNs), embeddings and polysemy, and low‑compute fine‑tuning strategies. It is commonly asked to probe both theoretical understanding and practical engineering judgment about convergence, variance, computational and memory complexity, representation learning, and resource‑constrained model adaptation; the domain is Machine Learning and the assessment spans conceptual understanding and practical application.
Machine LearningTechnical Screen - AmazonEasySoftware EngineerDescribe Delivering Under a Tight Deadline
This question assesses the candidate's ability to maintain high standards and operational excellence while working under significant time constraints, focusing on decision-making and communication.
Behavioral & LeadershipTechnical ScreenBehavioralLeadershipDelivery - AmazonMediumSoftware EngineerDescribe Linux and SQL experience
Prepare an engineering interview answer on Linux commands and SQL experience. Covers shell workflows, log debugging, file and process commands, SQL joins, aggregations, CTEs, window functions, and translating business questions into queries.
Software Engineering FundamentalsTechnical Screen - AmazonHardData ScientistDescribe Managing an End-to-End Project for Scalability
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Managing an End-to-End Project for Scalability states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistDescribe Overcoming Obstacles and Taking Calculated Risks
Evaluates Amazon behavioral interview readiness for calculated risk-taking and anticipated obstacles. Strong answers use STAR, explain risk calculus, data, mitigations, guardrails, and contingency plans, then quantify the outcome and reflect on what changed afterward.
Behavioral & LeadershipTechnical Screen - AmazonEasySoftware EngineerDescribe Work Beyond Your Formal Responsibilities
This question tests your initiative, ownership, and ability to collaborate across boundaries. It focuses on your capacity to identify organizational gaps and execute improvements without disrupting team alignment.
Behavioral & LeadershipTechnical ScreenBehavioralLeadershipOwnership - AmazonMediumData ScientistDescribe Your Most Challenging Project and Its Outcome
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Your Most Challenging Project and Its Outcome states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - AmazonMediumSoftware EngineerDescribe a Deep Debugging Investigation
Describe a deep debugging investigation through competing hypotheses, decisive evidence, causal reproduction, and a regression test for the real failure.
Behavioral & LeadershipTechnical Screen - AmazonEasyData ScientistDescribe a challenging project
This question evaluates a data scientist's Deliver Results competency, focusing on ownership, decision-making under ambiguity, trade-off analysis, stakeholder management, and the ability to deliver measurable business impact.
Behavioral & LeadershipTechnical Screen - AmazonEasySoftware EngineerDescribe a complex problem you solved
This question evaluates a software engineer's complex problem-solving, prioritization, cross-team collaboration and leadership competencies, along with practical use and validation of generative AI tools in engineering workflows.
Behavioral & LeadershipTechnical Screen
Amazon