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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 721–750 of 3640 results
- Capital OneMediumData ScientistAnalyze your favorite app and improve it
This question evaluates a data scientist's competencies in product analytics, monetization analysis, competitive positioning, metric specification (north-star and guardrails), and experimental design including power and validity considerations.
Analytics & ExperimentationOnsite - Capital OneMediumMachine Learning EngineerAnswer leadership scenarios with STAR
This question evaluates leadership, influence, problem-solving, prioritization, resilience, and the ability to deliver measurable results under constraints by eliciting STAR-format behavioral examples relevant to a Machine Learning Engineer role.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistAssess Cultural Fit and Soft Skills in Interviews
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Assess Cultural Fit and Soft Skills in Interviews states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistAssess Customer Value with Varied Contract Terms and Costs
Evaluates subscription contract unit economics across free periods, service costs, marketing costs, churn penalties, and fixed overhead. Strong answers compute customer value, portfolio value, and break-even acquisitions.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistAudit flight data quality from metadata
This question evaluates data quality auditing competencies, including validation of timestamps and timezones, detection of label leakage and inconsistent units, provenance and schema enforcement, and hands‑on use of SQL/Python for dataset inspection.
Data Manipulation (SQL/Python)Onsite - Capital OneMediumData ScientistAutomate Python Virtual Environment Setup on Linux Terminal
This question evaluates proficiency in shell scripting and Linux terminal automation for Python virtual-environment setup, focusing on environment management, automation, and reproducibility skills relevant to data science workflows.
Coding & AlgorithmsOnsite - Capital OneMediumSoftware EngineerBenefits of a Customer-Facing Chatbot for a Bank and Its Customers
A case-study opener asking what benefits a customer-facing chatbot would bring to a bank and to its customers. It tests whether you can tie each benefit to a concrete mechanism and metric, and weigh it against risks specific to banking such as wrong answers about money, authentication and privacy.
Product Design & StrategyOnsite - Capital OneEasyData ScientistBuild House Price Model Responsibly
This problem tests your ability to design a robust, fair, and scalable machine learning pipeline for house price prediction, emphasizing data preprocessing, model selection, and ethical oversight.
Machine LearningOnsiteSupervised LearningRegressionFairness - Capital OneMediumSoftware EngineerBuild a Banking System with Activity Rankings
This problem tests your ability to design a system that maintains account state and dynamically updates an ordered leaderboard of account activity using efficient data structures.
Coding & AlgorithmsOnsiteHash MapBalanced BSTDesign - Capital OneHardData ScientistBuild a causal ML pipeline end-to-end
This question evaluates expertise in causal inference and policy learning, covering ATE/CATE estimation, heterogeneous treatment effect methods, off‑policy evaluation, overlap and sensitivity diagnostics, and operational deployment with fairness guardrails in the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistBuild and evaluate donation propensity model
This question evaluates a data scientist's competency in profit-oriented predictive modeling, including conversion and donation-amount modeling, handling sparse and categorical features, calibration and uplift estimation, and offline policy evaluation under selection bias.
Machine LearningOnsite - Capital OneMediumSoftware EngineerBurner Card Numbers: Trade-offs, Hand Checks and a Buggy Validator
A three-part fintech case: weigh the trade-offs of disposable card numbers, check sample numbers by hand against format, Luhn and issuer-range rules, then find three bugs in a filter that rejects valid numbers and accepts invalid ones.
Software Engineering FundamentalsOnsitedebuggingluhnproduct-sense - Capital OneEasyData ScientistClean and Merge Housing Data
This question tests the ability to clean messy housing datasets, handle inconsistent data types, and perform efficient left-joins with external reference data.
Data Manipulation (SQL/Python)OnsitePandasData CleaningSQL - Capital OneMediumData ScientistCompute Customer Spend and Engineer Features for 2023
This question evaluates proficiency in SQL-based temporal aggregation and Python feature engineering on transactional datasets, including computing monthly spend summaries and engineering category-level spending features.
Data Manipulation (SQL/Python)Onsite - Capital OneEasyData ScientistCompute Optimal Die Re-roll Strategy
This question tests the ability to calculate expected values and determine optimal decision thresholds in a multi-stage probabilistic game.
Statistics & MathOnsiteProbabilityExpected ValueDecision Theory - Capital OneMediumData ScientistCompute credit-card portfolio profit and breakeven
This question evaluates financial modeling, unit-economics calculation, breakeven analysis, and basic risk/expected-loss estimation relevant to a Data Scientist, testing competency in statistics & math and algebraic derivation within financial analysis.
Statistics & MathOnsite - Capital OneMediumData ScientistCompute expansion profits and expected value
This question evaluates financial modeling, expected-value decision-making, probability reasoning, and sensitivity analysis skills applied to revenue, variable and fixed cost interactions and contractual fee impacts, aimed at a Data Scientist role.
Statistics & MathOnsite - 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 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 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 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 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 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 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 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 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 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 One