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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 31–60 of 263 results
- AnthropicMediumSoftware EngineerIdentify the Most Important AI Safety Risk
Tests your ability to analyze AI alignment failure modes—specifically sycophancy and emotional over-reliance—and formulate rigorous evaluation metrics, training mitigations, and product-level guardrails.
Machine LearningHR ScreenAI SafetyRLHFSycophancy - AnthropicMediumSoftware EngineerPrepare for a Recruiter Motivation Screen
Tests your ability to articulate clear, credible motivations for joining an AI frontier lab, explain your current career inflection point, and justify your primary technical tooling choices during an initial recruiter screen.
Behavioral & LeadershipHR ScreenBehavioralRecruiter ScreenCareer Motivation - AnthropicMediumData ScientistTell Me About a Metric You Built That Changed a Real Decision
A behavioral prompt asking the candidate to walk through a time they invented or redefined a metric, prove it was measuring the right thing, and show it actually changed what the business did next.
Behavioral & LeadershipHR Screenbehavioralmetricscommunication - AnthropicMediumMachine Learning EngineerWalk Through Your Work Background and Explain Your Concerns About AI Safety
A recruiter screen question for a machine learning engineering role at an AI research company: give a concise overview of your work background, then explain the concerns you have about AI safety. It tests whether you can tell a focused career story and discuss safety risks with specific mechanisms, calibrated views and a link to the role.
Behavioral & LeadershipHR Screen - AnthropicMediumSoftware EngineerWhy Work on AI Safety
Assesses your motivation and understanding of AI safety, focusing on how engineering skills intersect with frontier risks, mitigation strategies, and intellectual honesty.
Behavioral & LeadershipHR ScreenAI SafetyBehavioralCulture Fit - AppleMediumData ScientistClarify status, education, and multi-role strategy
This question evaluates a candidate's ability to document and communicate U.S. work authorization details, resolve resume inconsistencies, articulate multi-level role positioning, and demonstrate minicase availability and preparation—assessing attention to detail, clarity of communication, stakeholder coordination, and practical readiness for data-science interviews. Commonly asked in Behavioral & Leadership screens for Data Scientist roles to verify eligibility, alignment with role expectations, and logistical readiness, it tests both conceptual understanding of career-leveling and contingency planning and practical application of documentation, messaging, and time-management.
Behavioral & LeadershipHR Screen - ArmEasySoftware EngineerExplain GPU pipeline and execution hazards
This question evaluates understanding of GPU architecture concepts including the graphics pipeline from vertices to pixels, register read-after-write hazards in pipelined execution, memory behavior, throughput considerations, and execution scheduling, reflecting competency in computer graphics and parallel processor design.
Software Engineering FundamentalsHR Screen - ArmEasySoftware EngineerExplain your fit and motivation
This question evaluates behavioral and leadership competencies—motivation, role fit, initiative, problem-solving, ownership, and communication—by asking for concrete examples and outcomes.
Behavioral & LeadershipHR Screen - Axq CapitalMediumSoftware EngineerExplain What a Neural Network Is and How It Learns
Explain what a neural network is: how units and layers turn weighted inputs into outputs, why non-linear activations are essential, and how the weights are learned with a loss function, backpropagation, and gradient descent. It tests core machine learning fundamentals, including overfitting and when simpler models are the better choice.
Machine LearningHR Screen - Axq CapitalMediumSoftware EngineerHow Do You Handle Challenges? Answer with One Concrete Example
Answer the behavioral question of how you handle challenges, supported by one specific example of a difficult problem you faced at work. It tests structured storytelling, clear ownership of actions, breaking problems down, judgment about when to ask for help, and reflection on results and lessons learned.
Behavioral & LeadershipHR Screen - Axq CapitalMediumSoftware EngineerRow-Wise vs Column-Wise Array Reads, L1/L2 Caches, and the TLB
Compare horizontal and vertical reads of a large two-dimensional array, explain how L1 and L2 caches differ from main memory, and describe what the TLB does and what a TLB miss costs. It tests understanding of cache lines, locality, prefetching, address translation, and practical fixes for memory-bound code.
Software Engineering FundamentalsHR Screen - Boston Consulting GroupEasySoftware EngineerAnswer common behavioral questions using STAR
This question evaluates interpersonal communication, self-reflection, problem-solving, teamwork, and leadership competencies by prompting concrete examples of prior behavior and decision-making in workplace situations.
Behavioral & LeadershipHR Screen - Boston Consulting GroupEasySoftware EngineerDiscuss languages, packages, and tools you know
This question evaluates familiarity with programming languages, libraries/frameworks, and development tooling, along with the ability to apply a technology stack to deliver measurable impact.
Software Engineering FundamentalsHR Screen - ByteDanceMediumData EngineerChoose Components for a Data Warehouse
This question tests the ability to distinguish between distributed data processing frameworks, storage engines, and relational databases, evaluating your understanding of trade-offs in large-scale data infrastructure.
Software Engineering FundamentalsHR ScreenData EngineeringDistributed SystemsData Warehousing - ByteDanceMediumData EngineerDesign a Data Service for Downstream Consumers
Design a centralized, governed data serving platform that provides downstream consumers with stable query APIs, schema evolution safety, cursor-based exports, and data freshness metadata.
System DesignHR ScreenData EngineeringAPI DesignData Governance - ByteDanceHardBackend EngineerExplain Backend Fundamentals and AI Tooling
This question tests foundational knowledge across systems engineering, database design, version control, and modern AI integration strategies.
Software Engineering FundamentalsHR ScreenSystems DesignDatabasesGit - ByteDanceMediumData EngineerModel Data for Analytics, Reporting, and Applications
Tests your ability to process raw semi-structured event logs into a clean, dimensional metric table suitable for downstream analytics and reporting.
Data Manipulation (SQL/Python)HR ScreenData ManipulationData ModelingPython - ByteDanceHardBackend EngineerSolve Bracket Matching and Tree Width
This problem tests string manipulation and tree traversal techniques by requiring the validation of complex bracket sequences and the calculation of the maximum horizontal distance between nodes at the same level of a binary tree.
Coding & AlgorithmsHR ScreenStacksBFSTrees - Capital OneEasyData ScientistCalculate Annual Profit of Credit Card Portfolio
Evaluates annual profit calculation for a credit-card portfolio with monthly and annual revenue and cost components. Strong answers annualize figures, compute per-card profit, and scale to portfolio profit.
Statistics & MathHR Screen - Capital OneEasyData ScientistCalculate Incremental Customers for Marketing Spend Justification
Evaluates incremental customer calculations for partnership marketing spend justification. Strong answers derive break-even customers for fixed spend and required volume for variable-cost campaigns against a profit target.
Statistics & MathHR Screen - Capital OneEasyData ScientistCalculate Profitability and Break-Even for Lyft Partnership Campaign
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 Calculate Profitability and Break-Even for Lyft Partnership Campaign states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationHR Screen - Capital OneMediumData ScientistCompute capacity, staffing trade-offs, and break-even
This question tests the ability to model operational costs, evaluate staffing trade-offs between overtime and contracting, and perform break-even analysis under varying volume assumptions.
Analytics & ExperimentationHR ScreenCapacity PlanningCost AnalysisOperations - Capital OneMediumData ScientistConsider Key Factors Before Launching New Credit Card
Evaluates launch-readiness analysis for a new consumer credit card across market, customer, risk, compliance, operations, and financial viability. Strong answers validate assumptions and define go/no-go criteria.
Behavioral & LeadershipHR 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 OneEasyData ScientistDifferentiate fixed and variable costs with examples
This question evaluates understanding of cost classification and unit economics—specifically the distinction between fixed and variable costs—and the competency to apply those concepts within a subscription streaming business context.
Statistics & MathHR Screen - Capital OneMediumData ScientistExplain MSE vs MAE, AUC, and imbalance handling
This question evaluates a candidate's understanding of regression loss trade-offs (MAE vs MSE), classification evaluation metrics under severe class imbalance (ROC-AUC vs PR-AUC), neural network output activation and loss dynamics, and practical techniques for imbalance mitigation and probability-threshold selection in binary classification.
Machine LearningHR Screen - Capital OneEasyData ScientistGraph WTP vs content and explain cap
This question evaluates understanding of piecewise linear functions, marginal analysis, and the economic interpretation of willingness-to-pay, within the Statistics & Math domain for a Data Scientist role.
Statistics & MathHR Screen - Capital OneMediumSoftware EngineerGrid State After a Given Number of Days of Spoilage Spreading to Neighbors
A grid holds empty cells, fresh items and spoiled items, and each day spoilage spreads from every spoiled item to its edge-adjacent fresh neighbors. Given the grid and a number of days that may be very large, return the grid after exactly that many days. It tests simultaneous multi-source spreading, day-by-day timing, and unreachable cells.
Coding & AlgorithmsHR Screen - Capital OneMediumProduct ManagerHow do you make data-driven decisions?
Explain how to make data-driven product decisions by defining the decision, primary metrics, diagnostics, guardrails, data quality, experiments, segmentation, uncertainty, reversibility, and stakeholder communication.
Product / Decision MakingHR Screen - Capital OneMediumData ScientistImpute missing values without leakage
This question evaluates proficiency in data imputation, strict train/validation leakage prevention, temporal per-user propagation rules, and categorical aggregation for model-ready features.
Data Manipulation (SQL/Python)HR Screen
Anthropic
Apple
Arm
Boston Consulting Group
Capital One