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 781–810 of 3640 results
- Capital OneMediumData ScientistIdentify Risks and Improve Imputation Class Implementations
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 Identify Risks and Improve Imputation Class Implementations states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - Capital OneMediumSoftware EngineerImplement deposit, withdraw, and transfer in a class
This question evaluates a candidate's competence in concurrent programming, atomic state updates, and class-level account management for basic monetary operations.
Coding & AlgorithmsOnsite - Capital OneMediumProduct ManagerImprove Capital One Shopping
Improve Capital One Shopping as a PM case focused on profitable deal discovery. Covers monetization, personalized recommendations, coupon trust, email A/B testing, UX critique, metrics, segmentation, and guardrails.
Product Design & StrategyOnsite - Capital OneMediumData ScientistInterpret regression metrics and assumptions
This question evaluates a candidate's mastery of multiple linear regression diagnostics and interpretation, covering standardized coefficient interpretation and significance, R²/adjusted R² and out‑of‑fold RMSE comparisons, multicollinearity detection and its effect on coefficients and SEs, heteroskedasticity diagnostics and robust remedies, and implications of intercept omission and partial standardization. Commonly asked in data scientist interviews within the Statistics & Math domain because it probes both conceptual understanding of statistical inference and practical application of model diagnostics and adjustments for reliable prediction and inference.
Statistics & MathOnsite - Capital OneMediumSoftware EngineerMake the Case for a Banking Virtual Assistant: Who Wins and How?
Argue what an in-app virtual assistant would deliver to a retail bank and to its account holders, backing each claim with a mechanism, a metric and the money-specific risks that offset it.
Product Design & StrategyOnsiteproduct-sensefintechchatbots - Capital OneMediumSoftware EngineerMake the Case for a Customer-Facing Insurance Claims Chatbot
A case-study opener: argue what benefits a customer-facing chatbot would bring to an insurer and to its policyholders. Tests whether you anchor each benefit to a concrete mechanism and metric, and weigh it against the specific risks of giving automated answers about claims and money.
Product Design & StrategyOnsiteproduct-sensecase-studyrisk-tradeoffs - Capital OneMediumData ScientistMap stakeholders and influence routes
This question evaluates stakeholder mapping, influence routing, RACI design, and communication strategy competencies for coordinating multiple internal recruiters, hiring managers, and interview stakeholders in a hiring process.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistMitigate risk if no team matches
This question evaluates contingency planning, stakeholder management, negotiation skills, and professional communication by probing how a candidate responds when team-matching stalls and how they preserve leverage and recruiter engagement.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistModel network-service unit economics and breakeven
This question evaluates a data scientist's ability to model unit economics, calculate per-customer contribution and breakeven, and conduct sensitivity and probabilistic churn analysis.
Statistics & MathOnsite - Capital OneMediumSoftware EngineerNew-Grad Behavioral Round: Teamwork, Conflict, and a Failure
A new-grad onsite behavioral round asking for three separate stories from your own project work: one on teamwork, one on a disagreement with someone else, and one on a real failure. Tests whether you can tell a specific, honest story with your own contribution clearly visible, rather than a generic or deflected answer.
Behavioral & LeadershipOnsitebehavioralstar-methodnew-grad - Capital OneMediumData ScientistOptimize Pricing Strategy to Achieve Profitability and Market Growth
Evaluates pricing and go-to-market strategy for an unprofitable cloud-service startup. Strong answers diagnose unit economics, package by segment, assess market-share effects, and choose a pricing strategy with risks.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistOptimize invites under capacity constraints
This question evaluates a candidate's competency in probabilistic expected-value optimization, algorithmic efficiency for large-scale inputs, and constrained combinatorial selection including grouped all-or-nothing decisions and cost modeling.
Coding & AlgorithmsOnsite - Capital OneHardData ScientistOptimize theme park queues and revenue
This question evaluates a data scientist's skills in experiment design, causal inference under interference, metric definition and operational analytics for balancing wait-time reductions with revenue guardrails.
Analytics & ExperimentationOnsite - Capital OneMediumSoftware EngineerPlace Pieces on a Grid
This question evaluates spatial reasoning, matrix and array manipulation, collision detection, and stateful simulation skills required to implement ordered placement of shaped pieces on a grid.
Coding & AlgorithmsOnsite - Capital OneMediumSoftware EngineerPrepaid Travel Card Case Study: Checks, Trade-offs, Buggy Filter
A three-part case study on prepaid travel cards: weigh the trade-offs of issuing them, manually check sample numbers against length, Luhn, and prefix rules, then debug Python filter code whose conditionals let invalid numbers through. Tests product reasoning, careful manual rule-checking, and evidence-driven debugging.
Software Engineering FundamentalsOnsitecase-studyvalidationdebugging - Capital OneHardData ScientistPrioritize six improvements for a favorite app
This question evaluates a data scientist's product-thinking, prioritization, impact-estimation, experimentation design, and risk-mitigation competencies, framed within the Behavioral & Leadership domain and focused on product management, modeling, and experimentation.
Behavioral & LeadershipOnsite - Capital OneMediumSoftware EngineerProject Stories on Teamwork, Conflict and Failure for a New-Grad Behavioral Round
A new-grad behavioral round built on stories from your own projects, covering teamwork, a conflict with a teammate or stakeholder, and a failure, each told in Situation, Task, Action, Result form. It tests personal ownership, how you handle disagreement and whether you learn from your mistakes.
Behavioral & LeadershipOnsite - Capital OneMediumData ScientistQuantify and Model Flight Delays Using Statistical Tests
Evaluates flight-delay quantification, modeling, and mitigation testing using historical operational data. Strong answers define delay metrics, model delay probability and severity, avoid leakage, and design valid experiments.
Statistics & MathOnsite - Capital OneMediumData ScientistQuantify and optimize team-match funnel
This question evaluates a data scientist's skills in analytics and experimentation design, focusing on funnel metric definition, conversion and timing analysis, and A/B test planning within the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - Capital OneMediumSoftware EngineerReason About and Validate Virtual Card Transactions
Implement a policy validation engine for encoded virtual card numbers against incoming transactions, handling rule parsing, Luhn checksum verification, and structured status reporting.
Software Engineering FundamentalsOnsiteStringsValidationClean Code - Capital OneMediumProduct ManagerRecommend Build vs Buy for Restaurants
Solve a build-versus-buy PM case for a restaurant website builder. Covers segmentation, monetization, per-customer profitability, external reservation integration versus in-house build, adoption-adjusted profit, and strategic tradeoffs.
Product / Decision MakingOnsite - Capital OneMediumProduct ManagerRedesign the DMV Experience
Redesign the DMV customer experience with a product discovery framework. Covers pain points, problem statement, pre-visit readiness flow, storyboard, metrics, pilot plan, accessibility, and operational tradeoffs.
Product Design & StrategyOnsite - Capital OneMediumData ScientistRefactor code and enforce robustness
This question evaluates proficiency in code refactoring, robustness, input validation, unit testing, environment specification, and complexity analysis for Python data-processing workflows (pandas), within the Coding & Algorithms domain for Data Scientist roles.
Coding & AlgorithmsOnsite - Capital OneMediumSoftware EngineerResolve Conflict and Set a Team Objective
Tests your ability to resolve technical and interpersonal disagreements constructively and establish measurable, value-driven team objectives.
Behavioral & LeadershipOnsiteBehavioralConflict ResolutionLeadership - Capital OneMediumMachine Learning EngineerReturn all root-to-leaf tree paths
This question evaluates competence with binary tree structures, path enumeration and traversal techniques along with the ability to represent traversal results as formatted strings.
Coding & AlgorithmsOnsite - Capital OneEasyData ScientistReview Preprocessing Code and Tests
This problem tests your ability to identify common pitfalls in data science pipelines, specifically concerning data leakage, handling missing values, and software engineering best practices for reproducibility.
Coding & AlgorithmsOnsiteData PreprocessingData LeakageCode Review - Capital OneMediumData ScientistShould Company Launch Vegan Burger Based on Profit Analysis?
This question evaluates unit-economics and quantitative product-launch decision-making skills, focusing on contribution margin metrics, break-even analysis, profit and profit-margin calculations in the Analytics & Experimentation domain for Data Scientist roles, and is primarily a practical application of symbolic and numeric financial modeling with conceptual economic reasoning. It is commonly asked to assess how candidates translate per-unit prices, variable and fixed costs, and observed volumes into go/no-go judgments by reasoning about profitability, required volume targets, and sensitivity to cost or demand trends.
Analytics & ExperimentationOnsite - Capital OneMediumData ScientistValidate Unit-Test Coverage and Identify Missing Scenarios
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Validate Unit-Test Coverage and Identify Missing Scenarios states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - Capital OneMediumMachine Learning EngineerValidate virtual credit card transactions from encoded IDs
This question evaluates understanding of virtual credit card product trade-offs and the ability to design data models and deterministic validation logic for encoded transaction attributes, testing competencies in payment systems, data encoding/decoding, and rule-based validation.
System DesignOnsite - Capital OneMediumSoftware EngineerVirtual Card Case Study: Trade-offs, Card Number Rule Checks, and Debugging a Filter
A three-part virtual credit card case study: weigh the benefits and costs of giving merchants virtual card numbers, check five sample numbers against length, Luhn checksum and prefix rules, and debug a Python filter whose conditionals accept invalid numbers. It tests product reasoning, careful manual analysis and evidence-driven debugging.
Software Engineering FundamentalsOnsite
Capital One