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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 2731–2760 of 3640 results
- Palo Alto NetworksMediumSoftware EngineerFind normalized list difference efficiently
This question evaluates competency in computing normalized list differences, including string normalization, collection comparison, duplicate handling, and time and space complexity reasoning.
Coding & AlgorithmsOnsite - Palo Alto NetworksMediumSoftware EngineerMirror a binary tree and analyze complexity
This question evaluates understanding of binary tree manipulation and traversal methods, emphasizing recursion and iterative approaches as well as the ability to reason about time and space complexity.
Coding & AlgorithmsOnsite - ParafinHardSoftware EngineerDesign a loan application system
Design a loan application system evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - ParafinHardSoftware EngineerDesign loan application system
This question evaluates architecture and systems engineering competencies related to building scalable, compliant financial platforms, including data modeling, API and event-driven workflow design, security, and regulatory integration.
System DesignOnsite - PayPalMediumData ScientistAnalyze Transactions for Risk and Implement Mitigation Strategies
Analyze Transactions for Risk and Implement Mitigation Strategies 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 & ExperimentationOnsite - PayPalHardData ScientistAssess card transactions and plan risk strategy
Assess card transactions and plan risk strategy 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 & ExperimentationOnsite - PayPalMediumData ScientistBoost User Login Rate: Key Metrics to Monitor
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 Boost User Login Rate: Key Metrics to Monitor states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - PayPalEasyData ScientistCalculate Probability of Heads in Coin Flip Experiment
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Probability of Heads in Coin Flip Experiment states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - PayPalMediumData ScientistClean and Summarize User Purchase Data Efficiently
This question evaluates proficiency in data cleaning, aggregation, and time-series manipulation within the Data Manipulation (SQL/Python) domain, focusing on filtering by group counts and calculating inter-event timing metrics.
Data Manipulation (SQL/Python)Onsite - PayPalMediumData ScientistCompare WHERE vs HAVING with aggregates
Compare WHERE vs HAVING with aggregates evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Data Manipulation (SQL/Python)Onsite - PayPalMediumData ScientistCount Word Frequency and Print Top Three Words
This question evaluates proficiency in basic Python programming, string/text processing, frequency counting and sorting algorithms, reflecting core competencies for data manipulation tasks.
Coding & AlgorithmsOnsite - PayPalEasyData ScientistDecide if a dice-betting game is favorable
This question evaluates understanding of probability theory, expected value computation, risk-neutral decision criteria, and basic Monte Carlo simulation concepts within the Statistics & Math domain, testing both conceptual understanding of discrete probabilistic outcomes and practical application of simulation-based verification.
Statistics & MathOnsite - PayPalEasyData ScientistDescribe career goals and what makes good teams
This Behavioral & Leadership interview prompt for a Senior Data Scientist evaluates leadership, cross-functional influence, strategic communication, career planning, and team-operating competencies by requesting a data-driven project example, a 3–5 year career outlook, and definitions of effective managers and teams.
Behavioral & LeadershipOnsite - PayPalMediumSoftware EngineerDesign a Cross-Border Money Transfer Service
This question evaluates proficiency in designing distributed, fault-tolerant payment systems with competencies in payments flows, exchange-rate management, compliance and regulatory constraints, transactional integrity, idempotency, scalability, reliability, and observability.
System DesignOnsite - PayPalMediumSoftware EngineerDesign a Payment Fraud Detection Service
This system design question tests the ability to architect a real-time, low-latency fraud decisioning service that combines deterministic rule engines with machine learning model scores. It evaluates practical understanding of distributed systems trade-offs, feature store design, and safe model/rule deployment on a high-availability critical path.
System DesignOnsite - PayPalHardMachine Learning EngineerDesign a traditional fraud detection system
This question evaluates a Machine Learning Engineer's competency in end-to-end ML system design for real-time payments fraud detection, including labeling under delayed confirmations, handling extreme class imbalance and sampling, feature engineering across behavioral, graph, device and merchant signals, model selection for latency and scale, and production scoring and monitoring architecture. It is commonly asked in the ML System Design category to assess how an engineer balances low-latency decision-making with delayed sparse labels, calibration and threshold trade-offs, operational scalability and resiliency, and drift/adversarial detection, testing both conceptual understanding and practical application.
ML System DesignOnsite - PayPalMediumData ScientistDesign and Analyze A/B Test for Cashback Program
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 Design and Analyze A/B Test for Cashback Program states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - PayPalMediumData ScientistDesign elevator scheduling for small building
Design elevator scheduling for small building evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - PayPalEasyData ScientistDesign metrics and experiment for donation feature
A PayPal data science onsite case asking you to design metrics and a randomized experiment for an optional checkout donation feature on a food-ordering platform. Covers goal and hypothesis framing, a primary/diagnostic/guardrail metric hierarchy with precise definitions, user-level experiment design with power and variance-reduction, confounder/marketplace-bias handling, and a launch decision framework.
Analytics & ExperimentationOnsite - PayPalMediumData ScientistExplain Window Functions and Joins in SQL and Python
This question evaluates proficiency in SQL and Python data manipulation, covering window functions, join semantics (INNER, LEFT, RIGHT, FULL, CROSS), ranking functions, group and string aggregation, type casting, and building row-wise accumulations in Python, and it falls under the Data Manipulation (SQL/Python) category.
Data Manipulation (SQL/Python)Onsite - PayPalEasyData ScientistExplain confounding with an Uber example
This Statistics & Math interview question for a Data Scientist tests understanding of confounding and causal inference in observational data. Candidates must define a confounder, give a concrete non-demographic Uber example identifying the exposure, outcome, and confounder with the direction of bias, and describe at least two mitigation methods along with the assumptions each requires.
Statistics & MathOnsite - PayPalEasyData ScientistExplain p-values and interpret regressions
A PayPal Data Scientist onsite statistics rapid-fire covering p-values (plain-language and formal definitions, common misinterpretations, and an A/B-test decision at p=0.03), interpreting linear-regression coefficients under confounding (associational vs causal, 'controlling for' failure modes, and bias-reduction methods), and L1 vs L2 regularization with how to choose the penalty strength. It tests both statistical depth and the ability to communicate to product and technical audiences.
Statistics & MathOnsite - PayPalMediumData ScientistExplain past experience and role fit
Explain past experience and role fit evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - PayPalHardData ScientistExplain unsupervised fraud and evaluation
Explain unsupervised fraud and evaluation 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 - PayPalMediumData ScientistHow to validate production models?
This question assesses the end-to-end strategy for validating a production-ready machine learning model, focusing on rigorous testing frameworks, drift detection, and business-risk mitigation.
Machine LearningOnsiteModel ValidationProduction MLFraud Detection - PayPalMediumData ScientistIdentify Session with Maximum Overlapping Sessions Count
This question evaluates temporal interval reasoning, set-based querying and aggregation skills, focusing on identifying and counting overlapping time intervals using SQL or equivalent Python data-manipulation tools.
Data Manipulation (SQL/Python)Onsite - PayPalMediumData ScientistIdentify Users with Specific Page Visit Sequence
This question evaluates event-sequence detection and temporal ordering skills in SQL, focusing on concepts such as windowing, adjacent-row reasoning, and session-scoped grouping to identify ordered page visits.
Data Manipulation (SQL/Python)Onsite - PayPalMediumData ScientistInfluence Stakeholders Without Authority: Strategies and Examples
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Influence Stakeholders Without Authority: Strategies and Examples states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - PayPalMediumData ScientistMaster A/B Testing: Key Concepts and Methodologies Explained
Evaluates practical A/B testing and causal inference fundamentals for online products. Strong answers cover p-values, errors, power, workflow, Simpson's paradox, metrics, and observational causal methods.
Analytics & ExperimentationOnsite - PayPalMediumMachine Learning EngineerMaximize ones with limited flips
This question evaluates proficiency with array algorithms, particularly sliding-window and two-pointer reasoning, complexity analysis, and the ability to adapt solutions for streaming data.
Coding & AlgorithmsOnsite
Palo Alto Networks
PayPal