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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 2041–2070 of 9403 results
- Capital OneHardData ScientistDesign A/B test for credit card offer
This question evaluates a data scientist's competency in experimental design, causal inference, metric definition and measurement windows, power analysis, bias mitigation, identity resolution, and regulatory compliance for financial-product A/B testing.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistDesign ML deployment with GitHub and Jenkins
This question tests the ability to design an end-to-end MLOps pipeline, focusing on CI/CD orchestration, model versioning, reproducibility, and security compliance in a regulated financial environment.
Machine LearningOnsiteMLOpsCI/CDSystem Design - Capital OneMediumSoftware EngineerDesign Nearest-Pickup Search with Time
Design efficient nearest-pickup search for latitude-longitude data and a time-aware extension while clarifying distance, ties, result order, and temporal ranking.
Software Engineering FundamentalsOnline Assessment - Capital OneMediumSoftware EngineerDesign a Banking Component with Transfers and Activity Rankings
Design an in-memory banking service supporting account creation, deposits, atomic transfers, and activity rankings driven by a string-based command interface.
Software Engineering FundamentalsOnsiteDesignHash TableSorting - Capital OneMediumSoftware EngineerDesign a Buy-Now-Pay-Later Checkout Backend: Latency and PII Storage
Design the backend of a buy-now-pay-later checkout financing service that approves or declines a purchase in real time against a shopper's revolving credit line, then go deep on latency budgeting for the decision and on securely storing borrowers' personal data.
System DesignTechnical Screensystem-designfintechconcurrency - Capital OneMediumSoftware EngineerDesign a Credit Card System: Authorization Latency and Sensitive Data Storage
A system design question that asks for the backend of a credit card system, then goes deep on what payment authorization latency is reasonable and how to achieve it, and on how to store cardholders' sensitive personal information. It tests transaction flow design, available-credit concurrency, latency budgeting, and data protection.
System DesignTechnical Screen - Capital OneMediumSoftware EngineerDesign a Credit Card System: Authorization, Fraud, Bureau Reports, Limit Increases
A system design question for a credit card platform covering customer login, real-time payment authorization with fraud checks, monthly credit bureau reporting, a spending overview, and instant credit limit increase decisions. It tests laying out entities, services and read and write paths before choosing deep dives.
System DesignTechnical Screen - Capital OneMediumData ScientistDesign a better water bottle and test it
This question evaluates experimental design, product analytics, hypothesis formulation, metric selection, sample size/power/MDE estimation, segmentation, and risk and rollback planning within the Analytics & Experimentation domain for Data Scientist roles.
Analytics & ExperimentationOnsite - Capital OneMediumSoftware EngineerDesign a highly reliable account balance system
This question evaluates a candidate's ability to architect a highly reliable transactional account-balance service, testing competencies in data modeling and storage choice, API design for idempotency, concurrency and consistency mechanisms, replication/failover, partitioning for scale, and auditability.
System DesignOnsite - Capital OneHardData ScientistDesign a production face recognition system
This question evaluates production ML system design and engineering skills specific to face recognition, covering on-device model selection and embedding design, training objectives and evaluation protocols, anti-spoofing and robustness mechanisms, privacy and template protection, performance/resource constraints, fairness and monitoring, and safe experimentation. It is asked in the Machine Learning domain for Data Scientist roles because it assesses trade-offs between privacy, security, latency, and scalability in real-world systems, and it tests both conceptual understanding of biometric and security principles and practical application for deploying and operating on-device ML.
Machine LearningOnsite - Capital OneHardData ScientistDesign a profit growth strategy
This question evaluates a data scientist's competency in product analytics, marketplace economics, pricing strategy, demand elasticity estimation, experimentation, and quantitative impact sizing.
Analytics & ExperimentationTechnical Screen - Capital OneHardSoftware EngineerDesign a scalable banking system
This question evaluates system design skills focused on distributed systems architecture, transactional integrity, strong consistency models, security and authentication, external payment and ATM integrations, data modeling, API design, concurrency control, and operational monitoring for financial platforms.
System DesignOnsite - Capital OneMediumSoftware EngineerDesign a transaction class for deposits/withdrawals
This problem tests your ability to design a robust, thread-safe class for managing bank account transactions, focusing on atomicity and data consistency.
Software Engineering FundamentalsTechnical ScreenConcurrencyObject-Oriented DesignThread-Safety - Capital OneMediumSoftware EngineerDesign an Enterprise Agent Platform with Guardrails and Latency Control
Design an enterprise agent platform with durable execution, deliberate workflow-versus-agent choices, enforceable tool guardrails, and a dependency-aware latency plan.
System DesignOnsite - Capital OneHardData ScientistDesign and analyze a card signup A/B test
This question evaluates a data scientist's competency in A/B test design, causal inference, statistical power and sample-size calculation, risk-adjusted profit metrics, sequential testing, identity resolution, and monitoring for credit and fraud impacts.
Analytics & ExperimentationTechnical Screen - Capital OneHardData ScientistDesign and analyze ad A/B test
This question evaluates a candidate's competency in online experimentation and statistical analysis, covering hypothesis formulation, variance reduction, clustering and stratification, multiplicity control, power/MDE calculation, and operational metric guardrails.
Analytics & ExperimentationOnline Assessment - Capital OneHardData ScientistDesign and analyze ads A/B test this week
This question evaluates experimental design and applied statistical analysis skills for A/B testing, covering randomization, exposure control, stratification, variance reduction methods, metric formalization, hypothesis testing, sample sizing, sequential monitoring, diagnostics, and handling delayed offline conversions.
Analytics & ExperimentationOnline Assessment - Capital OneHardData ScientistDesign and analyze an SBA mini case experiment
This question evaluates expertise in experimental design and causal inference, including sample-size and power calculations, ITT/TOT analysis, variance reduction techniques, heterogeneity measurement, data quality and fraud handling, guardrail monitoring, and rollout decision-making for product experiments.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistDesign cloud product packaging and pricing strategy
This question evaluates a candidate's product strategy and quantitative business competencies, including pricing and packaging design, unit economics, risk assessment, and the ability to communicate trade-offs to executives.
Behavioral & LeadershipTechnical Screen - Capital OneMediumData ScientistDesign fundraising experiment and guardrails
This question evaluates experimental design, statistical power and sample-size calculation, selection of primary and guardrail metrics, segmentation with multiple-testing control or hierarchical modeling, stopping rules for sequential analysis, and revenue-impact calculations for A/B testing of email solicitations.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistDesign metrics and an A/B test for an app
This question evaluates product analytics and experimentation competencies—product framing, unit economics, revenue modeling, computation-ready metric design, statistical power/sample-size calculation, A/B test setup, and cost-benefit/breakeven analysis—for a Data Scientist role in the Analytics & Experimentation domain, and is commonly asked to assess the ability to translate product hypotheses into measurable business outcomes and defend decisions under stakeholder scrutiny. It tests both conceptual understanding of segmentation, metric validity, and bias considerations and practical application in defining precise numerators/denominators, computing minimum sample sizes and test duration, and producing actionable ROI models.
Analytics & ExperimentationTechnical Screen - Capital OneMediumMachine Learning EngineerDesign model deployment, monitoring, and low-latency inference
This question evaluates competency in ML system design and production engineering—covering model deployment and versioning, safe rollouts and rollbacks, monitoring of service health, data quality/drift, model performance and business metrics, and latency optimization for low-latency inference.
ML System DesignOnsite - Capital OneHardData ScientistDesign profit evaluation for loyalty program
This question evaluates a data scientist's competency in causal inference, experimental and quasi-experimental design, incremental profit measurement from transaction-level data, statistical power and sample-size estimation, and validation and guardrail metric planning.
Analytics & ExperimentationTechnical Screen - Capital OneEasyData ScientistDesign robber detection from surveillance video
This question assesses the ability to design an end-to-end computer vision pipeline for detecting rare, high-stakes events in real-time video surveillance.
Machine LearningTechnical ScreenComputer VisionSystem DesignAnomaly Detection - Capital OneMediumData ScientistDetermine Claim Rate for Breakeven in Insurance Portfolio
Evaluates weather-insurance portfolio profitability from premiums, servicing, regulatory costs, and expected claims. Strong answers derive break-even claim rate, segment-selection rules, and incremental profit formulas.
Statistics & MathOnsite - Capital OneMediumData ScientistDetermine Country with Most 'Sunny' Days
This question evaluates proficiency in data aggregation, grouping, and filtering to summarize categorical time-series records using SQL or equivalent Python data-manipulation tools, and it belongs to the Data Manipulation domain with an emphasis on practical application of query and aggregation techniques.
Data Manipulation (SQL/Python)Onsite - Capital OneMediumData ScientistDetermine Factors Influencing Airline Flight Delays Statistically
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Determine Factors Influencing Airline Flight Delays Statistically states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - Capital OneMediumData ScientistDetermine Optimal Budget Allocation for Maximum Profit
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Determine Optimal Budget Allocation for Maximum Profit states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistDetermine Rent Price Factors for Multi-Family Apartment Profitability
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Determine Rent Price Factors for Multi-Family Apartment Profitability states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistDetermine intersection dimension of two 4D subspaces
This question tests the application of the Grassmann Dimension Formula to determine the possible dimensions of the intersection of two subspaces within a larger vector space.
Statistics & MathTechnical ScreenLinear AlgebraVector SpacesSubspaces
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