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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
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- 11
- Topics
- 8
- Languages
Showing 9001–9030 of 9403 results
- VisaMediumProduct ManagerProduct Roadmap & Stakeholder Trade-offs
Practice explaining an end-to-end product roadmap process with stakeholder trade-offs. The solution covers outcome-driven planning, input collection, prioritization frameworks, alignment rituals, Now/Next/Later planning, and a concrete example balancing sales, risk, engineering, and customer needs.
Product / Decision MakingTechnical Screen - VisaMediumSoftware Engineer IQuery the Top Ten Customers by Recent Spend
This problem tests proficiency in SQL aggregation, window functions, and joining across normalized transactional tables to perform time-bound analysis.
Data Manipulation (SQL/Python)Online AssessmentSQLAggregationJoins - VisaMediumData ScientistResolve Team Conflicts: Actions and Outcomes Explored
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Resolve Team Conflicts: Actions and Outcomes Explored states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - VisaMediumSoftware EngineerShortest Prefix of a Digit String That Can Build Every Permutation of a Target
Given a digit string and a list of digit targets, return for each target the length of the shortest prefix whose characters could build every permutation of that target, or -1 if no prefix can. It tests character frequency counting, precomputation over a string, and answering many queries efficiently.
Coding & AlgorithmsOnline Assessment - VisaMediumSoftware EngineerSolve Three Array Coding Problems
This multi-part question evaluates algorithmic problem-solving skills in array manipulation, optimization (maximizing capped contributions), modular combinatorics (counting divisible-sum triplets), and sequence selection/dynamic decision-making for longest selectable non-decreasing subarrays.
Coding & AlgorithmsOnline Assessment - VisaMediumProduct ManagerTell me about yourself and trade-offs
Prepare Visa PM recruiter answers for self-introduction, why Visa, and stakeholder tradeoffs. The solution uses Present-Past-Future and STAR structures, emphasizing payments scale, trust, compliance, risk, conversion, and cross-functional product judgment.
Behavioral & LeadershipTechnical Screen - VmwareMediumSoftware EngineerCompute iterative sliding-window sum reduction
This question tests the ability to implement a sliding window algorithm to compute dynamic reductions, focusing on efficient array traversal and state maintenance.
Coding & AlgorithmsTechnical ScreenSliding WindowAlgorithmsArray - VoleonMediumSoftware EngineerAdvance Cyclic Ball Flips Efficiently
Calculate the final binary states of cyclic nodes visited by multiple advancing markers in O(n + m) time using interval difference arrays and modular arithmetic.
Coding & AlgorithmsTechnical ScreenPrefix SumArraysMath - VoleonEasyData ScientistAnalyze a Regression Dataset and Build a Safe File-Processing Utility
Implement an atomic, non-destructive file-cleaning utility for tabular logs and a greedy collinearity filter to screen regression features based on target correlation.
Data Manipulation (SQL/Python)Technical ScreenPythonPandasFeature Engineering - VoleonMediumData ScientistAnalyze time-zoned events with pandas
This question evaluates proficiency in data manipulation and analysis with pandas and SQL, covering timezone-aware timestamp handling, event deduplication, cohort and retention metrics, currency parsing and FX conversion, revenue imputation, ARPU calculation, and scalability for high-volume event data.
Data Manipulation (SQL/Python)Technical Screen - VoleonMediumMachine Learning EngineerAudit and Extend a Time-Series ML Pipeline
Audit a time-series ML pipeline for leakage, tune with chronological validation, and add inference with saved preprocessing and timestamp-correct features.
Machine LearningTechnical Screen - VoleonMediumQuantitative ResearcherBound R-Squared for Individual and Combined OLS Regressions
Derive in-sample OLS R-squared bounds and the joint-model range from individual scores, including intercept assumptions, predictor correlation, and out-of-sample exceptions.
Statistics & MathTechnical Screen - VoleonHardMachine Learning EngineerBuild a Tool-Using Weather Assistant
Design a grounded weather assistant with typed model tool calls, server-side credentials, validated live data, provenance, and safe failure behavior.
Software Engineering FundamentalsTechnical Screen - VoleonHardData ScientistBuild a regularized regression pipeline
This question evaluates a candidate's competency in building an end-to-end regularized regression pipeline in scikit-learn, covering feature engineering and polynomial expansion, categorical encoding, target‑transform diagnostics, penalized linear model selection and tuning (Ridge/Lasso/ElasticNet), cross‑validation strategies including TimeSeriesSplit, leakage prevention, nested evaluation, feature effect ranking, and model persistence. Commonly asked in Machine Learning interviews for Data Scientist roles, it tests practical application skills in the Machine Learning domain and requires both hands-on implementation proficiency and conceptual understanding of validation design, target transformations, and interpretability, with emphasis on practical application augmented by conceptual judgment.
Machine LearningTechnical Screen - VoleonHardData ScientistCompute robust inference under skew and outliers
This problem tests your ability to design a robust statistical framework for comparing two groups with highly skewed, heavy-tailed data and potential outliers, moving beyond simple t-tests.
Statistics & MathTechnical ScreenStatisticsA/B TestingRobust Inference - VoleonMediumSoftware EngineerCount queen attacks on points with blockers
This question evaluates spatial reasoning on an integer 2D grid, geometric line relationships for queens' attack paths, and the use of efficient data structures to count interactions and handle occlusion, in the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - VoleonMediumData ScientistDescribe Your Machine Learning Project Experience
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 Describe Your Machine Learning Project Experience states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningHR Screen - VoleonHardMachine Learning EngineerDesign a cold-start video recommender
This problem tests the ability to architect a recommendation system that provides personalized content for new users or items with zero historical interaction data.
ML System DesignTechnical ScreenRecommender SystemsCold StartMulti-Armed Bandits - VoleonHardData ScientistDesign and diagnose a regression pipeline
This question evaluates a data scientist's ability to design and diagnose an end-to-end regression pipeline for zero-inflated, heavy-tailed targets, focusing on feature engineering for high-cardinality variables, handling multicollinearity, choosing appropriate loss/distribution and regularization, time-aware validation, uncertainty quantification, interpretability, and data leakage mitigation. It is commonly asked in the Machine Learning domain to test practical application alongside conceptual understanding of regression modeling, distributional assumptions, cross-validation strategies, and performance versus interpretability trade-offs.
Machine LearningTechnical Screen - VoleonMediumSoftware EngineerDesign circular sliding-window ratio tracker
This question evaluates proficiency with sliding-window techniques, handling of circular arrays and modular indexing, design of constant-time update mechanisms, and rigorous algorithmic time/space complexity analysis.
Coding & AlgorithmsTechnical Screen - VoleonMediumData ScientistDiagnose and interpret regression assumptions
This problem tests your ability to diagnose violations of OLS assumptions in count data and determine the appropriate corrective modeling strategy.
Statistics & MathTechnical ScreenRegressionGLMDiagnostics - VoleonMediumData ScientistDiscuss Résumé Highlights and Past Work Experience.
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Discuss Résumé Highlights and Past Work Experience. states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipHR Screen - VoleonHardMachine Learning EngineerEstimate Fine-Tuning Memory Requirements
Learn to estimate full and parameter-efficient fine-tuning memory with a transparent ledger for model state, activations, sharding, and peak overhead.
ML System DesignTechnical Screen - VoleonHardMachine Learning EngineerExplain ML and statistical modeling
This problem tests your theoretical understanding of statistical modeling and machine learning, specifically focusing on the implications of loss function selection, handling heteroskedasticity, and the trade-offs between bias and variance in predictive modeling.
Machine LearningTechnical ScreenStatisticsRegressionOptimization - VoleonMediumData ScientistExplain P-Value Reporting and Bootstrap for Coefficient Estimation
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Explain P-Value Reporting and Bootstrap for Coefficient Estimation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - VoleonHardMachine Learning EngineerExplain a Markov Decision Process
Review the components and trajectory semantics of an MDP, including state sufficiency, policies, transitions, rewards, and discounted return.
Machine LearningTechnical Screen - VoleonMediumQuantitative ResearcherExplain a Sorting Algorithm Such as Merge Sort: Mechanics, Correctness, Complexity
Explain a sorting algorithm of your choice, such as merge sort, in a technical screen for a quantitative role. Tests whether you can describe the split and merge steps, trace a small example, argue correctness, derive time and space complexity, and discuss stability and alternatives.
Software Engineering FundamentalsTechnical Screen - VoleonHardMachine Learning EngineerExplain the Bellman Equation
Understand Bellman expectation and optimality equations through one-step return decomposition, fixed points, and reinforcement-learning algorithms.
Machine LearningTechnical Screen - VoleonHardMachine Learning EngineerFine-Tune a Language Model on Question-Answer Pairs
Review the full workflow for supervised language-model fine-tuning on question-answer pairs, including masking, leakage control, evaluation, and rollout.
Machine LearningTechnical Screen - VoleonMediumData ScientistFit Linear Regression: Analyze Economic Impact of Coefficients
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 Fit Linear Regression: Analyze Economic Impact of Coefficients states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen
Visa
Voleon