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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 91–120 of 3640 results
- AirtableHardSoftware EngineerDesign JSON serialization for circular references
This question evaluates understanding of deterministic, cross-language JSON serialization and deserialization for object graphs with cycles, including concepts such as cycle detection, stable object identifiers, preservation of shared subobjects, traversal-order determinism, input validation/security, and time/space complexity.
System DesignOnsite - AirtableMediumSoftware EngineerDesign NL-to-Formula assistant for Airtable
Airtable software-engineer onsite ML system design question: design an assistant that turns natural-language requests into the product's formula language, grounded in the live table schema, and optionally applies them to the base via the Airtable API. It tests LLM orchestration with structured outputs / a typed IR, schema grounding and type checking, ambiguity handling, static + dynamic formula validation, prompt-injection safety, confirmation-gated writes, latency trade-offs, and offline/online evaluation.
ML System DesignOnsite - AirtableMediumSoftware EngineerDesign a scalable search service with sharding
This question tests the ability to design a distributed search architecture capable of handling high-throughput indexing and low-latency queries across a massive dataset.
System DesignOnsiteDistributed SystemsSearch IndexingSharding - AirtableMediumSoftware EngineerDesign an async API with idempotency
This question tests the ability to design a distributed asynchronous processing system that ensures idempotency, data consistency, and reliable task execution under high concurrency.
System DesignOnsiteAPIIdempotencyAsync - AirtableMediumSoftware EngineerExplain how to make robust HTTP API calls
This problem tests your understanding of building resilient client-side code that interacts with external HTTP services, focusing on failure handling and operational stability.
Software Engineering FundamentalsOnsiteAPI DesignReliabilityHTTP - AirtableMediumSoftware EngineerImplement BFS serializer and deserializer
This question evaluates the skill of serializing and deserializing Python object graphs using BFS, including handling primitives, dictionaries, shared subobjects, and circular references while preserving object identity.
Coding & AlgorithmsOnsite - AirtableEasySoftware EngineerOptimize a dispatcher’s scheduling data structures
This question evaluates understanding of algorithmic analysis and data-structure design for memory-constrained scheduling, including efficient lookup and update operations for tables and machines and the selection of placement and migration policies, categorized under Software Engineering Fundamentals.
Software Engineering FundamentalsOnsite - AirwallexMediumBackend EngineerBar-Raiser Project Deep Dive: Defending Decisions, Trade-offs, Impact and Lessons
A bar-raiser round that deep-dives into one of your past projects, probing each decision through layers of why, the trade-offs accepted, the measured impact and the lessons learned. Tests ownership, judgment, honest reflection and consistency under repeated follow-up questions.
Behavioral & LeadershipOnsite - AirwallexMediumData ScientistCompute Moderation View Metrics
An Airwallex Data Scientist onsite SQL question on content-moderation analytics. You write three queries over a per-day view table and a one-to-many post/violation-type policy-score table: distinct posts with 10+ views in the last 7 days, Nudity view prevalence over the last 30 days, and a monthly violation-type distribution. The core challenge is windowed aggregation plus avoiding view double-counting across violation types.
Data Manipulation (SQL/Python)Onsite - AirwallexMediumData ScientistDiagnose a dip in approval/conversion rate
This Analytics & Experimentation prompt evaluates time-series diagnostics, ratio decomposition (numerator/denominator and traffic mix), segmentation and data-quality triage, and causal inference skills at a staff-level applied analytics abstraction.
Analytics & ExperimentationOnsite - AirwallexEasyData ScientistDiagnose conversion-rate time series and CTA swap
This question evaluates a data scientist's competency in time-series diagnosis, conversion-rate decomposition, causal inference, and A/B experiment design within the Analytics & Experimentation category of Data Science.
Analytics & ExperimentationOnsite - AirwallexMediumData ScientistHow do you resolve partner conflict?
This question evaluates a data scientist's cross-functional collaboration, stakeholder management, conflict resolution, influencing without formal authority, and data-driven communication skills.
Behavioral & LeadershipOnsite - AirwallexMediumData ScientistHow would you measure billboard impact?
This problem tests your ability to design a quasi-experimental framework for measuring the offline impact of an OOH (Out-of-Home) advertising campaign on digital conversion metrics.
Analytics & ExperimentationOnsiteCausal InferenceMetricsExperimental Design - AirwallexMediumData ScientistHow would you test swapping two CTA buttons?
This question evaluates A/B test design, causal inference, funnel metric selection and diagnostic/guardrail thinking within Analytics & Experimentation for a Data Scientist role, and is commonly asked to gauge the ability to reason about user behavior, conversion trade-offs, and risk management in sensitive domains like fintech.
Analytics & ExperimentationOnsite - AirwallexMediumData ScientistInvestigate Harassment Surge and Mitigation
An Airwallex Data Scientist onsite analytics question: a monthly moderation report shows the Harassment violation type spiking, and you must decide whether the surge is real or a measurement artifact, then propose and test mitigations. It probes diagnostic analytics, prevalence-metric design, denominator effects, Simpson's paradox, selection bias, label drift, model calibration, causal inference (ITS/DiD), and experiment design with guardrails.
Analytics & ExperimentationOnsite - AmazonMediumSoftware EngineerA Problem That Needed Deep Analysis: Knowing You Focused on the Right Things
A behavioral question about a problem that required in-depth thought and analysis, how you knew you were focusing on the right things, and what the outcome was. Follow-ups probe precise details of the example, such as an LLM agent's token size, the effect of adding fields, and what completeness meant.
Behavioral & LeadershipOnsite - AmazonHardProduct ManagerAlexa Domain-Knowledge Data Pipelines
Practice an Alexa knowledge-pipeline system design covering global holidays, animal facts, source reliability, ingestion, calendar normalization, knowledge graphs, answer serving, entity linking, confidence thresholds, misrouted queries, and debugging.
Product / Decision MakingOnsite - AmazonMediumProduct ManagerAmazon Fit, Motivation & Leadership Principles
Practice Amazon Product Manager fit and behavioral questions covering why Amazon, why this PM role, changing direction mid-project, fast decisions with limited information, and five-year career goals. The solution emphasizes customer obsession, metrics, guardrails, ownership, and leadership principles.
Behavioral & LeadershipOnsite - AmazonMediumProduct ManagerAmazon Leadership Principles Behavioral Scenarios
A consolidated bank of 24 Amazon product manager onsite behavioral questions, covering dive deep and root-cause analysis, scaling, influence and disagreement, customer obsession, ownership, mistakes and missed commitments, decisions under ambiguity, Think Big, and one cloud-connectivity triage hypothetical. Each prompt comes with a worked STAR outline, quantified results, and the Leadership Principles the interviewer is scoring. Includes a story-bank map that collapses all 24 prompts into 10 to 12 reusable stories.
Behavioral & LeadershipOnsite - AmazonMediumProduct ManagerAmazon Leadership Principles – Behavioral Deep Dive
Full Amazon Product Manager onsite behavioral loop: 25 Leadership Principles STAR prompts collected from real interview reports, covering customer obsession, dive deep, data-heavy projects, bias for action, ownership, simplification, conflict, missed commitments, failure, and long-term trade-offs. Each prompt comes with what interviewers score, a metrics-backed model answer, and the pitfalls that sink candidates. Includes a story-bank strategy, a reusable STAR template, and the follow-ups to expect.
Behavioral & LeadershipOnsite - AmazonHardProduct ManagerAmazon New-Service Launch Cases
Practice Amazon new-service launch cases for table booking, flower delivery, and advertising platforms. The solution covers target users, JTBD, MVP scope, architecture, metrics, GTM, risks, Working Backwards framing, and long-term flywheels.
Product / Decision MakingOnsite - AmazonMediumBusiness Intelligence EngineerAnalyze Seller Compliance and Customer Purchase Patterns
This question evaluates a candidate's ability to manipulate and analyze temporal and categorical datasets, covering competencies such as event sequencing, set-based filtering, and cross-table aggregation in SQL/Python.
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistAnalyze Top 10 Items' Revenue Contribution by Category
This question evaluates proficiency in data manipulation and analytical querying—aggregation, window functions, ranking and revenue attribution—alongside conceptual knowledge of normalization versus denormalization and ETL pipeline design.
Data Manipulation (SQL/Python)Onsite - AmazonHardData ScientistAnalyze an A/B test over last 7 days
This question evaluates competency in A/B test analysis and experiment design, covering conversion-rate computation, statistical inference (two-proportion testing and confidence intervals), sample-ratio checks, heterogeneity assessment (fixed versus random effects), and power/MDE reasoning.
Analytics & ExperimentationOnsite - AmazonEasyData ScientistAnswer Amazon Leadership Principle Scenarios
This question evaluates behavioral competencies relevant to a Data Scientist intern, including leadership, communication, decision-making under uncertainty, risk assessment, time management, teamwork, learning agility, and motivation for the role.
Behavioral & LeadershipOnsite - AmazonMediumSoftware EngineerAnswer Amazon Leadership Principles questions
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Answer Amazon Leadership Principles questions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - AmazonMediumTechnical Program ManagerAnswer Amazon leadership behaviorals
Prepare senior Amazon L6 program-management behavioral answers for unconventional problem solving, stakeholder disagreement, ownership outside scope, team speed, and tough feedback. The solution emphasizes STAR structure, mechanisms, leadership principles, and measurable impact.
Behavioral & LeadershipOnsite - AmazonMediumData ScientistAnswer core behavioral questions for data roles
Answer core behavioral questions for data roles 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 & LeadershipOnsite - AmazonMediumData ScientistAssess Culture Fit Through Behavioral Interview Questions
Evaluates culture-fit behavioral answers for inventing and simplifying, customer communication, and innovation. Strong answers use STAR, show stakeholder judgment, quantify impact, and reflect on lessons.
Behavioral & LeadershipOnsite - AmazonMediumSoftware EngineerAssess cooldown and plan interview strategy
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Assess cooldown and plan interview strategy states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite
Airtable
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