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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 8881–8910 of 9403 results
- UpstartMediumSoftware EngineerDemonstrate Project Ownership
Practice showing genuine end-to-end ownership through problem framing, trade-offs, delivery, recovery, and operational follow-through.
Behavioral & LeadershipHR Screen - UpstartEasyData ScientistDerive logistic regression objective and gradients
This question evaluates understanding of logistic regression, maximum likelihood estimation, cross-entropy (negative log-likelihood), L2 regularization, and the ability to compute model gradients for binary classification.
Machine LearningHR Screen - UpstartMediumSoftware EngineerDescribe Your Python Experience
Present Python experience through maintained production work, correctness practices, operational judgment, and honest tool-selection limits.
Software Engineering FundamentalsHR Screen - UpstartMediumData ScientistDesign Algorithm for Longest Substring with K Distinct Characters
This question evaluates proficiency in string-processing and sliding-window algorithm patterns, assessing competency in designing efficient solutions and managing data structures to track distinct elements and reason about time and space complexity.
Coding & AlgorithmsOnsite - UpstartMediumData ScientistDesign Push-Notification System for Airport Surge Pricing
Design Push-Notification System for Airport Surge Pricing evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - UpstartHardData ScientistDesign a Real-Time Personalized Ad Selection System
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 Design a Real-Time Personalized Ad Selection System states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - UpstartEasyData ScientistDetermine distribution of aX+b when X~N(0,1)
This problem tests your understanding of how linear (affine) transformations affect the properties of a normal distribution and the derivation of its mean and variance.
Statistics & MathHR ScreenStatisticsProbabilityNormal Distribution - UpstartMediumSoftware EngineerDiscuss Compensation Expectations
Prepare a clear compensation-expectations response that asks for level and range context while considering the complete package.
Behavioral & LeadershipHR Screen - UpstartMediumData ScientistEstimate Family Proportions and Explain Regression Anomalies
Estimate Family Proportions and Explain Regression Anomalies 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 & MathOnsite - UpstartMediumData ScientistEstimate and Derive Regression Coefficient for X on y
Estimate and Derive Regression Coefficient for X on y 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 & MathOnsite - UpstartEasyData ScientistEstimate impact without experiments and pick variant
This question evaluates causal inference and experimental-analysis competencies in Analytics & Experimentation and Data Science, covering observational estimands, causal identification assumptions and biases, uncertainty quantification for A/B/C tests, multiple-comparisons reasoning, and post-launch forecasting and monitoring.
Analytics & ExperimentationTechnical Screen - UpstartHardData ScientistEvaluate channels and allocate budget
This question evaluates competency in marketing analytics and experimentation for a Data Scientist role, covering funnel analysis, conversion and CAC metrics, attribution modeling, response-curve-based budget allocation, and incremental test design using channel-level spend and outcome data.
Analytics & ExperimentationTechnical Screen - UpstartEasyData ScientistExplain L1 vs L2 and ridge vs lasso
This question evaluates understanding of regularization techniques in Machine Learning—specifically distinctions between L1 and L2 norms and their instantiation as Lasso and Ridge regression—covering effects on objective functions, parameter sparsity, geometric intuitions, handling of correlated features, and the role of feature scaling.
Machine LearningOnsite - UpstartHardData ScientistExplain Treatment Results and Recommend Launch Criteria for Experiments
Explain Treatment Results and Recommend Launch Criteria for Experiments evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - UpstartHardData ScientistExplain career moves and defend moat
This question evaluates a candidate's ability to narrate career progression with quantified outcomes, analyze and defend a company's sustainable competitive advantage (moat) using a strategic framework, and present high-visibility decisions and trade-offs under deadline, testing competencies in self-awareness, strategic reasoning, risk assessment, stakeholder communication, and metrics-driven impact reporting for a Data Scientist role in the Behavioral & Leadership category. It is commonly asked to assess how well a candidate synthesizes narrative and quantitative evidence for leadership and cross-functional contexts, and it targets both conceptual understanding of strategic frameworks and practical application of metrics and presentation trade-offs.
Behavioral & LeadershipTechnical Screen - UpstartHardData ScientistExplain tackling ambiguity and defending a decision
This question evaluates judgment under ambiguity, data-driven decision-making, stakeholder alignment, trade-off analysis, and post-decision learning in a data science context, and is categorized within Behavioral & Leadership interview topics.
Behavioral & LeadershipTechnical Screen - UpstartMediumSoftware EngineerFind Maximum Eastbound City Visits and Parse CSV
Tests string parsing for CSV records with quoted fields and escaped delimiters, followed by finding the longest valid subsequence of eastbound city visits with lexicographical tie-breaking.
Coding & AlgorithmsOnline AssessmentDynamic ProgrammingString ParsingAlgorithms - UpstartMediumSoftware EngineerFind max word letter span
This question evaluates proficiency in string processing, character mapping, and algorithmic analysis by requiring computation of per-word letter spans while accounting for case normalization, punctuation handling, ties, duplicates, and edge-case word forms.
Coding & AlgorithmsTechnical Screen - UpstartMediumSoftware EngineerFind the Minimum Absolute Difference
This problem tests your ability to efficiently find the minimum absolute difference between any two elements in an array, requiring an understanding of sorting and linear scans.
Coding & AlgorithmsOnline AssessmentSortingArraysAlgorithms - UpstartHardData ScientistFormulate hypotheses and metrics for video-pin ramp
This question evaluates experiment-design and data-science competencies including hypothesis specification, metric choice and guardrails, segmentation, interference handling, rollout planning, power/MDE considerations, and data-quality monitoring, and it falls under the Analytics & Experimentation domain.
Analytics & ExperimentationHR Screen - UpstartHardData ScientistHow to Architect a Personalized Ads Serving System
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 How to Architect a Personalized Ads Serving System states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - UpstartMediumData ScientistHow would you measure causal impact?
Answer causal-impact and three-variant experiment prompts with clear estimands, quasi-experimental assumptions, validation, uncertainty, by-hand CTP intervals, multiple-comparison caveats, business guardrails, and Bayesian forecasting for production conversion.
Analytics & ExperimentationTechnical Screen - UpstartEasyData ScientistIdentify binomial model and compute moments
This question tests the ability to identify a binomial process, express its probability mass function, and derive its fundamental moments (mean and variance).
Statistics & MathHR ScreenStatisticsProbabilityBinomial Distribution - UpstartMediumSoftware EngineerImplement Byte Formatting and Cafeteria Billing
This problem tests your ability to implement custom string formatting logic for file sizes and a stateful simulation of a billing system with capacity limits.
Coding & AlgorithmsOnline AssessmentStringsSimulationHashing - UpstartEasyData ScientistImplement factorial and count trailing zeros
This question evaluates understanding of factorial computation and algorithmic techniques, including iterative versus recursive approaches, handling large integers and recursion constraints, and efficient counting of trailing zeros via number-theoretic reasoning.
Coding & AlgorithmsOnsite - UpstartMediumData ScientistLeverage Existing Model for Low Credit Score Applicants
Leverage Existing Model for Low Credit Score Applicants evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - UpstartMediumData ScientistNavigate Conflicting Priorities in Cross-Functional Collaboration
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Navigate Conflicting Priorities in Cross-Functional Collaboration states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - UpstartMediumSoftware EngineerProfessional Introduction for a Recruiter Screen
Prepare a focused recruiter-screen introduction that links concrete engineering ownership to the role you want next.
Behavioral & LeadershipHR Screen - UpstartEasySoftware EngineerRemove Duplicate Integers While Preserving Order
This problem tests your ability to filter elements from an array while maintaining the original sequence and ensuring optimal time complexity using auxiliary data structures.
Coding & AlgorithmsOnline AssessmentArraysHash TableData Structures - UpstartMediumSoftware EngineerRemove Duplicates While Preserving First-Appearance Order
Return each array value once in its original first-appearance order, using membership tracking without sorting the result.
Coding & AlgorithmsOnline Assessment
Upstart