practice a little every day, paws crossed ♡
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 751–780 of 3640 results
- 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 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 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 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 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 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 OneEasyData ScientistDiagnose Flight Delays and Burger Launch
This question evaluates your ability to diagnose statistical modeling trade-offs on noisy operational flight data and perform a structured financial break-even analysis for a new lounge product launch.
Analytics & ExperimentationOnsiteregressioncausal-inferencebreak-even-analysis - Capital OneMediumData ScientistDiagnose Multicollinearity in Flight Delay Prediction Model
Evaluates flight-delay modeling, data quality checks, and multicollinearity diagnosis. Strong answers identify leakage and timestamp issues, choose classification or regression based on the target, diagnose VIF and correlated features, and mitigate multicollinearity based on prediction or interpretation goals.
Machine LearningOnsite - Capital OneHardData ScientistDiagnose and fix a flight-delay modeling setup
This question evaluates proficiency in statistical modeling for binary outcomes, including concepts such as appropriate model choice and link functions, categorical encoding, ETL-driven data quality issues, multicollinearity diagnostics (e.g.
Statistics & MathOnsite - Capital OneMediumData ScientistDiscuss Ethical Concerns of Facial-Recognition Technology Implementation
Evaluates Capital One behavioral and ethics interview readiness, including cross-functional teamwork, advanced analytical techniques, and facial-recognition login risks. Strong answers cover privacy, consent, biometric security, fairness, accessibility, compliance, governance, and customer-trust guardrails.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistDissect a failure and corrective actions
This question evaluates a candidate's competency in root-cause failure analysis, accountability, stakeholder impact quantification, postmortem documentation, and the design and verification of corrective controls relevant to a Data Scientist role.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistDraft a non-pushy follow-up email
This question evaluates written communication, professional etiquette, stakeholder management, and the ability to convey technical value succinctly in a Data Scientist hiring context.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistEstimate Default Rates Using Logistic Regression Model
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Estimate Default Rates Using Logistic Regression Model states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - Capital OneMediumData ScientistEvaluate Energy One's Transition to Renewable Energy Sources
Evaluates energy transition economics for moving from fossil fuels to renewables. Strong answers compare capex, fixed and variable costs, capacity, contribution margin, required MWh for target return, renewable technology trade-offs, policy incentives, reliability, and risk-adjusted recommendation criteria.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistEvaluate Factors Before Renewing TV-Series Contracts
Capital One analytics case on TV-series renewal and sale decisions, covering cost-revenue profit, qualitative factors, viewer value, opportunity cost, NPV, profitability actions, and keep-versus-sell economics.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistEvaluate Financial Feasibility of Ride-Sharing Service
Evaluates ride-sharing financial feasibility with pricing, capacity, driver costs, fixed costs, and competitive strategy. Strong answers compute driver capacity, daily revenue, driver cost, fixed cost, and profit, then assess pricing and utilization levers with marketplace health guardrails.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistEvaluate Groupon's Impact on Restaurant's Profitability and Strategy
Evaluates restaurant profitability from a Groupon-style voucher partnership under discount, commission, capacity, and incrementality assumptions. Strong answers model contribution margin, cannibalization, repeat value, and strategic deal terms.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistEvaluate Models for Credit-Risk Scoring at Capital One
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 Evaluate Models for Credit-Risk Scoring at Capital One states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - Capital OneMediumData ScientistEvaluate OutlierHandler Class for Code Quality and Testing
Evaluates code-review judgment for OutlierHandler and imputer preprocessing classes in ML pipelines. Strong answers explain fit-transform separation, leakage prevention, API quality, edge cases, and testing.
Machine LearningOnsite - Capital OneMediumData ScientistEvaluate Python Class Design in Data Pipeline
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 Evaluate Python Class Design in Data Pipeline states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - Capital OneMediumProduct ManagerEvaluate a Credit Card Partnership
Evaluate a Capital One credit-card merchant partnership. Includes segment-level direct P&L, discount cost, incremental card margin, strategic value, break-even customer calculation, and pilot recommendation.
Product / Decision MakingOnsite - Capital OneMediumData ScientistEvaluate a credit-card acquisition partnership
This question evaluates cohort-level unit economics, discounted cash-flow (NPV) modeling, and sensitivity/break-even analysis related to customer acquisition and credit-card portfolio economics.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistExplain App Growth Strategy and Key Performance Metrics
Evaluates app growth strategy communication in an analytics interview for consumer digital products. Strong answers explain the app, monetization, core metrics, roadmap, first experiment, ROI, and trade-offs for a manager audience.
Analytics & ExperimentationOnsite - Capital OneMediumMachine Learning EngineerExplain core ML concepts and lifecycle
This question evaluates understanding of core machine learning concepts and lifecycle competencies, including bias–variance tradeoff, parameters versus hyperparameters, batch versus real-time inference, strategies for training at scale, feature engineering, and end-to-end model development.
Machine LearningOnsite - Capital OneMediumData ScientistExplain helping-others trade-offs with evidence
This question evaluates a Data Scientist's leadership, collaboration, prioritization, stakeholder management, documentation practices, and ability to quantify impact and analyze trade-offs, framed in the Behavioral & Leadership category.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistHandle an irate passenger after flight delay
This question evaluates crisis communication, conflict de-escalation, resource-constrained decision-making, cross-functional coordination, and metrics definition for operational recovery, presented for a Data Scientist and categorized under Behavioral & Leadership.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistHandle extended team-matching uncertainty
This question evaluates a Data Scientist candidate's stakeholder management, escalation judgment, prioritization, and communication strategy when managing uncertainty across multiple recruiting teams.
Behavioral & LeadershipOnsite - Capital OneEasyMachine Learning EngineerHow would you improve card-type detection?
This question tests your ability to design a modular and maintainable classification system that identifies credit card networks (e.g., Visa, Mastercard) based on raw input data while considering validation and error handling.
Software Engineering FundamentalsOnsiteData ValidationString ManipulationSystem Design
Capital One