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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 4381–4410 of 4616 results
- VerkadaMediumSoftware EngineerImplement a Middle-Tier REST API Server for Camera Log Ingestion and Queries
Implement a middle-tier REST API server that lets cameras upload their logs and lets users request them back, with fake logs supplied by the tests. It tests endpoint design, request validation and status codes, storage organized for time-range queries, and thread-safe request handling.
Software Engineering FundamentalsTechnical Screen - VerkadaHardSoftware EngineerIntersect Motion Threshold Periods Across Cameras
Find maximal motion periods for one camera or the simultaneous intersection across many piecewise-constant camera streams. The prompt defines exact integer timestamps, threshold scaling, last-reading horizon, undefined prefixes, inclusive endpoints, and deterministic interval ordering for portable console verification.
Coding & AlgorithmsTechnical Screen - VerkadaMediumSoftware EngineerMerge Sorted Arrays In Place
This question evaluates array manipulation and in-place merging skills, specifically understanding sorted sequences, pointer/index management, and space-time trade-offs within array algorithms, and it falls under the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - VerkadaMediumSoftware EngineerStreaming Python Parser for Key-Value Log Lines with Quoted and Escaped Values
Write a Python program that streams a multi-gigabyte host log file and parses each line into a dict holding its timestamp and key-value fields. It tests tokenizing quoted values with spaces, inner equals signs and escaped quotes, handling two timestamp formats, skipping blank lines, and keeping memory constant with a generator.
Software Engineering FundamentalsTechnical Screen - VerkadaMediumSoftware EngineerValidate a 9×9 grid under constraints
This problem tests your ability to validate a 9x9 grid against Sudoku-like constraints using efficient lookups and index arithmetic.
Coding & AlgorithmsTechnical ScreenArraysMatrixHash Sets - Virtu FinancialMediumData ScientistCompute probability in urn with green returns
Compute probability in urn with green returns evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - Virtu FinancialMediumData ScientistCompute probability with colored balls draws
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Compute probability with colored balls draws states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - Virtu FinancialMediumData ScientistImplement Connect Four Winner Detection
This question evaluates the ability to design and implement game-state logic for a Connect Four-style board, covering board data structures, coordinate and boundary handling, gravity-based token placement, and efficient pattern detection for consecutive tokens in the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - Virtu FinancialMediumData ScientistMaximize Points by Buying Cost-Multiplier Cards
This problem tests your ability to determine an optimal ordering strategy and apply dynamic programming to maximize a cumulative score subject to specific operations.
Coding & AlgorithmsTechnical ScreenDynamic ProgrammingGreedyOptimization - VisaMediumSoftware EngineerCompute distinct sums from limited coins
This question evaluates algorithmic problem-solving skills in combinatorics and resource-constrained subset-sum computation, focusing on dynamic programming, optimization, and complexity analysis within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - VisaHardSoftware EngineerCount Subsequences That Move Between Positions
This problem tests dynamic programming skills, specifically the ability to count subsequences that satisfy a positional movement constraint based on element parity.
Coding & AlgorithmsTechnical ScreenDynamic ProgrammingMathArrays - VisaHardSoftware EngineerDesign an experiment assignment service
This question evaluates system-design and analytics competencies for online experimentation platforms in the Analytics & Experimentation domain, focusing on deterministic sticky assignment, configuration storage, eligibility targeting, traffic allocation, mutual exclusion, overrides, exposure logging, metric tagging, privacy/compliance, latency and availability targets, experiment lifecycle, and high-level architecture. It is commonly asked because it assesses both conceptual understanding and practical system-design application of distributed-system trade-offs, scalability, data integrity, operational reliability, and privacy constraints, and gauges the ability to define high-level APIs, architecture, and justify key trade-offs.
Analytics & ExperimentationTechnical Screen - 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 - 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 - 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 - 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 - 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
Verkada
Virtu Financial
Visa
Voleon