practice a little every day, paws crossed ♡
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 7021–7050 of 9403 results
- PlaystationMediumSoftware EngineerRefactor a Shopping Cart to Print Grouped, Promoted, Sorted Receipts
A 30-minute object-oriented exercise that turns a stream of item names into a printed receipt, then extends it to group items by type, apply a buy-three-get-two-free promotion and sort the lines alphabetically. It tests refactoring a shopping cart's storage and its add and remove methods, exact money handling and clean class design.
Software Engineering FundamentalsTechnical Screen - Plymouth Rock Assurance CorporationMediumData ScientistCalculate Ultimate Loss by Policy Term
This question evaluates a candidate's ability to perform sequence-aware cumulative aggregation and partitioned data manipulation, assessing competency in calculating running totals (ultimate loss) across policy-term records.
Data Manipulation (SQL/Python)Technical Screen - Point72HardData ScientistBuild the auction status table
This problem tests advanced SQL skills, specifically the ability to perform window functions, handle state transitions, and aggregate time-series data to reconstruct the final status of entities.
Data Manipulation (SQL/Python)Online AssessmentSQLWindow FunctionsData Aggregation - Point72MediumQuantitative ResearcherCalibrate the Risk-Aversion Parameter
Calibrate mean-variance risk aversion to target risk and out-of-sample utility while accounting for units, scaling, constraints, and regimes.
Statistics & MathOnline Assessment - Point72MediumQuantitative ResearcherChoose Practical Portfolio Constraints
Specify practical stat-arb constraints for neutrality, exposure, concentration, turnover, liquidity, and borrow, including feasibility checks.
Statistics & MathOnline Assessment - Point72MediumData ScientistClassify relationships for multiple circle pairs
This question evaluates computational geometry skills, careful integer arithmetic for numeric precision, case-based classification logic, and analysis of time and space complexity in the Coding & Algorithms domain for Data Scientist roles.
Coding & AlgorithmsTechnical Screen - Point72MediumQuantitative ResearcherCompare Missing-Value Strategies for Linear Models
Compare mean, random empirical, and indicator-assisted imputation for linear models under MCAR, MAR, and MNAR without training leakage.
Machine LearningOnline Assessment - Point72MediumQuantitative ResearcherCompare Two Equity Risk Models
Compare two equity risk models out of sample using forecast calibration, residual structure, stability, coverage, regimes, and portfolio impact.
Machine LearningOnline Assessment - Point72MediumData ScientistConvert Dates to Calendar Quarter Labels in SQL/Python
This question evaluates a candidate's ability to perform date/time data transformation and normalization in SQL or Python, focusing on converting raw numeric dates into standardized quarter labels for aggregation and reporting.
Data Manipulation (SQL/Python)Technical Screen - Point72MediumQuantitative ResearcherDefend a Quantitative Factor Research Project
Practice defending quantitative factor construction, information coefficient, model selection, backtesting, and research improvements.
Machine LearningTechnical Screen - Point72MediumQuantitative ResearcherDerive Annualized Portfolio Volatility
Derive portfolio volatility from factor covariance and stock-specific variance, then annualize it with consistent horizons and units.
Statistics & MathOnline Assessment - Point72HardQuantitative ResearcherDerive the Optimal Bid in a Two-Bidder Second-Price Auction
Two risk-neutral bidders compete for one item in a sealed-bid second-price auction. Explain the assumptions and derivation clearly, check edge cases, and show how the result changes when those assumptions no longer hold.
Statistics & MathTechnical Screen - Point72HardData EngineerDescribe Resolving Stakeholder Trade-Offs
This question evaluates stakeholder management, prioritization, trade-off analysis, and cross-functional communication skills within a data engineering context.
Behavioral & LeadershipTechnical Screen - Point72HardData EngineerDesign Data Quality and Observability Pipeline
This question evaluates proficiency in designing production data orchestration, data quality validation, and observability frameworks for both batch and streaming pipelines, covering dependency management, schema and integrity checks, and operational handling of anomalous records.
System DesignTechnical Screen - Point72MediumMachine Learning EngineerDesign Features for Residual Volatility
This problem tests the ability to decompose financial asset returns into systematic and idiosyncratic components and design features to capture residual volatility.
Machine LearningTechnical ScreenTime-SeriesFinanceFeature Engineering - Point72MediumData ScientistDetermine Circle Intersection Status for Rendering Order
This question evaluates geometric reasoning and computational-geometry competency, including spatial relationship analysis and handling numerical edge cases relevant to pairwise object interaction.
Coding & AlgorithmsTechnical Screen - Point72MediumQuantitative ResearcherEncode Categorical Variables Across Model Families
Choose categorical encodings for linear, tree, and neural models while handling intercept rank, unseen values, and high cardinality.
Machine LearningOnline Assessment - Point72MediumQuantitative ResearcherEstimate Factor Returns and Residual Returns
Derive weighted factor-return estimates from a sample, then compute residual returns across a larger trading universe with stable linear algebra.
Statistics & MathOnline Assessment - Point72HardSoftware EngineerEvaluate Defensive Testing and Pair Programming
Tests your ability to critically evaluate software quality practices—defensive automated testing versus pair programming—and construct a risk-adjusted, cost-effective engineering adoption strategy.
Software Engineering FundamentalsOnline AssessmentSoftware EngineeringTestingAgile - Point72MediumQuantitative ResearcherExplain Ensemble Learning and Its Main Families
Explain why combining multiple models can improve predictions. Cover data and labels, leakage-safe features, baselines and model choice, offline evaluation, deployment constraints, monitoring, and drift.
Machine LearningTechnical Screen - Point72MediumData ScientistExplain How L1 and L2 Regularization Change Linear-Model Coefficients
This question tests the conceptual understanding of how L1 and L2 penalties influence the optimization objective and the resulting structure of the weight vector in a linear regression model.
Machine LearningOnsiteRegularizationLinear RegressionOptimization - Point72HardData EngineerExplain Spark Execution and Optimization
This question evaluates a candidate's understanding of the Apache Spark execution model, lazy evaluation, distributed processing (including jobs, stages, tasks, and shuffles), and performance-tuning considerations for batch and streaming pipelines on columnar data lakes.
Software Engineering FundamentalsTechnical Screen - Point72MediumQuantitative ResearcherExplain Transformer Encoder and Decoder Behavior
This question evaluates knowledge of Transformer architecture and generative language modeling, specifically the encoder versus decoder roles, attention patterns and causal masking, plus mechanisms behind stochastic text generation such as sampling and temperature.
Machine LearningTechnical Screen - Point72HardData ScientistExplain and tune decision trees robustly
The question evaluates a candidate's understanding of CART decision tree mechanics, split criteria and surrogate splits for missing values, hyperparameter tuning and pruning, overfitting diagnostics, preprocessing for large feature sets and high‑cardinality categoricals, and criteria for choosing ensemble methods within the Machine Learning domain.
Machine LearningOnline Assessment - Point72EasyData ScientistFind kth missing integer and redundant operations
This paired question evaluates array and algorithmic skills — specifically order-statistics and missing-number reasoning for the k-th missing positive, and time-window aggregation with frequency tracking for detecting redundant log events — within the coding & algorithms domain.
Coding & AlgorithmsOnline Assessment - Point72MediumQuantitative ResearcherFind the Minimum in a Rotated Sorted Array
A strictly increasing array of distinct integers has been rotated at an unknown pivot. Work through the function contract, boundary cases, correctness argument, and time and space complexity expected in a production-quality solution.
Coding & AlgorithmsTechnical Screen - Point72MediumMachine Learning EngineerFind the Smallest String After One Decrement
This question evaluates string manipulation, lexicographic ordering, and reasoning about local character transformations including wrap-around behavior.
Coding & AlgorithmsTechnical Screen - Point72HardQuantitative ResearcherFind the Two Heaviest Balls with Minimum Comparisons
There are 32 balls with distinct weights. Explain the assumptions and derivation clearly, check edge cases, and show how the result changes when those assumptions no longer hold.
Statistics & MathTechnical Screen - Point72MediumQuantitative ResearcherFormulate a Single-Period Stat-Arb Objective
Formulate a convex single-period statistical-arbitrage objective balancing expected return against covariance risk, with consistent units.
Statistics & MathOnline Assessment - Point72HardData ScientistHow would you explain PCA and SHAP?
A Point72 Data Scientist technical screen asking you to defend one end-to-end ML project: problem framing and label timing, leakage controls, time-based validation, feature engineering, hyperparameter tuning, dimensionality reduction with PCA, and model interpretability with SHAP. Expects the PCA optimization objective and SVD link, the Shapley principle behind SHAP, plot interpretation, and the pitfalls of each.
Machine LearningTechnical Screen
Playstation
Point72