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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 2011–2040 of 9403 results
- Capital OneHardSoftware EngineerCount Subarrays Containing at Least k Disjoint Pairs of Equal Values
Count the contiguous segments of an integer array, framed as fruit types on a conveyor belt, that contain at least k pairs of equal values with no position used in two pairs. Tests reasoning about how the number of disjoint pairs changes as a segment grows, and efficient counting on inputs of up to 100,000 elements.
Coding & AlgorithmsOnline Assessment - Capital OneMediumSoftware EngineerCount Subarrays Whose Step-by-Step Changes Match an Up, Flat, Down Pattern
Count the fixed-length windows of an integer array whose consecutive rises, equal steps, and drops follow a given pattern of 1, 0, and -1 values, counting overlapping matches separately. Tests turning a comparison rule into an exact window check with correct index bounds.
Coding & AlgorithmsOnline Assessment - Capital OneMediumSoftware EngineerCount Three-Digit Numbers With All-Distinct Digits in a Range
A coding question that asks you to count the three-digit integers in an inclusive range whose hundreds, tens and units digits are all different from one another. It tests digit extraction, inclusive range boundaries and treating zero digits like any other digit.
Coding & AlgorithmsOnline Assessment - Capital OneMediumSoftware EngineerCount Windows That Follow an Increase, Equal, Decrease Pattern
A coding question that gives an integer array and a pattern of 1, 0 and -1 values standing for an increase, an equal step and a decrease, and asks how many windows of consecutive elements follow the pattern exactly. It tests translating pairwise comparisons into steps and counting overlapping matches efficiently.
Coding & AlgorithmsOnline Assessment - Capital OneMediumSoftware EngineerCount ordered pairs that concatenate to target
This question evaluates a candidate's ability to manipulate string and numeric representations, perform efficient counting, and leverage associative data structures to identify matching concatenations.
Coding & AlgorithmsOnline Assessment - Capital OneMediumData ScientistCritique a product interface and propose fixes
This question evaluates a candidate's product-thinking and usability analysis skills, specifically application of heuristics and accessibility standards alongside abilities in redesign proposal, success-metrics definition, and experiment/telemetry planning.
Behavioral & LeadershipTechnical Screen - Capital OneMediumData ScientistCritique and Test Python Preprocessing Utilities Effectively
This question evaluates a candidate's ability to review and critique Python preprocessing utilities, covering competencies in software design patterns (fit/transform separation), coding style and import hygiene, and unit-test specification for data-science pipelines.
Coding & AlgorithmsTechnical Screen - Capital OneMediumData ScientistDebug and test a Python function in venv
This question evaluates understanding of Python runtime semantics (mutable default arguments, in-place list mutation during iteration), debugging and unit-testing practices, virtual environment and packaging workflows, and CI/tooling considerations.
Coding & AlgorithmsOnsite - Capital OneMediumData ScientistDecide Which Show to Renew
This problem tests the ability to calculate expected value and ROI for a business decision under uncertainty, requiring the candidate to incorporate subscriber churn risk and lifetime value metrics.
Analytics & ExperimentationTechnical ScreenExpected ValueROIChurn Analysis - Capital OneMediumData ScientistDecide and test Groupon program incrementality
This question evaluates experimental design, causal inference, uplift and profitability modeling, operational risk assessment, and KPI-driven decision rules within the Analytics & Experimentation domain for data scientists, testing both conceptual understanding and practical application of A/B testing, sample-size considerations, and guardrail metrics. It is commonly asked to determine whether a candidate can translate business constraints into a rigorous experiment and profit-oriented analysis, balancing incremental impact estimation, cannibalization concerns, and operational trade-offs to support go/no-go decisions.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistDecide content volume and price under uncertainty
This question evaluates a candidate's competency in pricing and content-production optimization under uncertainty, including defining optimization objectives and constraints, specifying necessary metrics, and assessing sensitivity to changes in variable costs.
Analytics & ExperimentationHR Screen - Capital OneHardData ScientistDecide on vegan-burger R&D investment
This question evaluates quantitative business-case construction, including market sizing (TAM/SAM/SOM), unit economics and financial modeling, risk assessment and option valuation, plus experimental-design skills within the Analytics & Experimentation domain for a Data Scientist role.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData ScientistDecide whether to invest in R&D
This question evaluates a data scientist's competency in data-driven decision-making, financial modeling, product strategy, and cross-functional leadership by requiring synthesis of customer insight, NPV and cost analysis, cannibalization effects, operational risks, brand positioning, and a staged rollout plan.
Behavioral & LeadershipTechnical Screen - Capital OneMediumData ScientistDecide whether to sell both SKUs
This question evaluates a data scientist's competency in profit-impact modeling, break-even analysis, and accounting for cannibalization effects using unit contribution margins and fixed costs.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData EngineerDeep-dive XGBoost handling and overfitting
This question evaluates proficiency with gradient-boosted decision trees and related competencies such as native versus imputation handling of missing values, causes and control of overfitting via regularization and hyperparameters, selection of metrics and validation strategies for imbalanced outcomes, and practical debugging concerns like data leakage, time-based splits, and calibration for a Data Engineer role. It is commonly asked in Machine Learning interviews to assess both conceptual understanding of algorithm behavior and practical application of model evaluation and deployment-ready validation techniques.
Machine LearningTechnical Screen - Capital OneHardData ScientistDefine and validate an airline profitability metric
This question evaluates competency in metric design, business analytics, causal inference, and experimental design by requiring a decomposable airline route profitability metric, operational guardrails, validation via backtests and sensitivity analyses, and a policy experiment plan.
Analytics & ExperimentationOnsite - Capital OneHardData ScientistDemonstrate cross-functional leadership with data and reflection
This question evaluates cross-functional leadership, data-driven decision-making, stakeholder influence without formal authority, and the ability to trade off speed, risk, and user experience within a data scientist role in the Behavioral & Leadership category.
Behavioral & LeadershipTechnical Screen - Capital OneMediumData ScientistDemonstrate ownership and navigate challenges
This question evaluates a data scientist's ownership, leadership, and technical modeling competencies, covering end-to-end project leadership, prioritization and trade-offs under time pressure, incident and conflict management, and the ability to translate model metrics and feature attributions for non-technical stakeholders.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistDescribe Effective Team Collaboration and Ethical Decision-Making Strategies
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Effective Team Collaboration and Ethical Decision-Making Strategies states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - Capital OneMediumProduct ManagerDescribe a Product You Led
Prepare a STAR behavioral answer about leading a technical product end to end. Covers ownership, stakeholder alignment, technical tradeoffs, measurable impact, and lessons learned.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistDescribe accomplishment, failure, and helping others
This question evaluates a data scientist's behavioral and leadership competencies—ownership, impact measurement, learning from failure, stakeholder management, mentoring, and decision-making—by requiring three concrete examples with dates, metrics, constraints, actions, outcomes, and manager feedback.
Behavioral & LeadershipTechnical Screen - Capital OneMediumData ScientistDescribe an accomplishment with quantified impact
This question evaluates leadership, stakeholder alignment, project management, and data-driven impact quantification competencies within a Data Science role.
Behavioral & LeadershipTechnical Screen - Capital OneHardData ScientistDescribe best team and complex project
This question evaluates leadership, cross-functional collaboration, conflict resolution, project ownership, technical decision-making, risk mitigation, and metrics-driven delivery within data science and analytics projects, and is categorized as Behavioral & Leadership for Data Scientist roles.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistDescribe handling an urgent ad-hoc request
This question evaluates a data scientist's competency in handling urgent ad-hoc analytics requests, covering stakeholder communication, prioritization, rapid data validation, trade-off decisions, and quantifying impact under tight deadlines; it is categorized under Behavioral & Leadership for Data Scientist roles.
Behavioral & LeadershipOnsite - Capital OneMediumSoftware EngineerDescribe leadership challenges and managing a difficult report
This question evaluates leadership, conflict-resolution, and team-management competencies in a software engineering context by probing examples of leading projects and managing a difficult or noncompliant direct report, and is categorized under Behavioral & Leadership.
Behavioral & LeadershipOnsite - Capital OneMediumMachine Learning EngineerDescribe projects, conflicts, and tough stakeholders
This question evaluates project ownership, technical troubleshooting, cross-team communication, conflict resolution, and stakeholder management competencies for a Machine Learning Engineer within the Behavioral & Leadership domain.
Behavioral & LeadershipOnline Assessment - Capital OneMediumData ScientistDescribe resolving ambiguity under pressure
This question evaluates the ability to resolve ambiguity under pressure by combining quantitative reasoning, assumption alignment, stakeholder communication, and leadership in framing analysis.
Behavioral & LeadershipOnsite - Capital OneEasyData ScientistDescribe your best team and your role
This Capital One data scientist behavioral question asks you to describe your ideal team environment and the best team you've been part of—what made it effective, the role you played, how you influenced outcomes, and how you handled disagreement or ambiguity. It evaluates self-awareness, collaboration maturity, ownership, and cross-functional fit.
Behavioral & LeadershipTechnical Screen - Capital OneMediumData ScientistDescribe your proudest accomplishment and impact
This question evaluates a data scientist's leadership, impact measurement, accountability, and communication competencies, including the ability to quantify outcomes, separate individual versus team contributions, articulate constraints and risks, and produce repeatable artifacts.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistDesign A/B Test for Marketing Campaign Impact Evaluation
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 Design A/B Test for Marketing Campaign Impact Evaluation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite
Capital One