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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.
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Showing 1531–1560 of 4616 results
- EtsyMediumData ScientistImplement streaming autocomplete under tight memory
This question evaluates a candidate's ability to design a memory- and latency-constrained autocomplete service, testing skills in data structure selection, string/Unicode normalization, approximate string matching, concurrency, dynamic updates, and algorithmic time/space analysis.
Coding & AlgorithmsTechnical Screen - EvenUpMediumData ScientistBuild a model to predict wine quality
This question evaluates predictive modeling and data-science competencies—exploratory data analysis, feature assessment, problem framing (regression vs classification vs ordinal), model selection and validation, evaluation metric choice, and post-model feature-importance interpretation—in the Machine Learning domain, combining conceptual understanding with practical application. It is commonly asked in technical interviews for Data Scientist roles because it tests end-to-end modeling judgment, reasoning about data distributions and variable usefulness, selection of validation and evaluation strategies, and awareness of pitfalls such as collinearity and data leakage.
Machine LearningTechnical Screen - EvenUpMediumSoftware EngineerWalk through your resume and impacts
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Walk through your resume and impacts states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - ExaHardMachine Learning EngineerDebug a PyTorch Contrastive Loss
Repair a PyTorch contrastive loss whose matrix multiplication contracts incompatible dimensions instead of producing pairwise scores. Validate aligned rank-two tensors and temperature, create the correct batch-by-batch logits, preserve gradients, and handle low-precision numerical stability.
Machine LearningTechnical Screen - ExaHardMachine Learning EngineerDesign a Streaming Percentile Estimator
Design a streaming percentile estimator without storing every observation, and define rank error precisely. Compare a deterministic single-stream summary with t-digest-style centroids across memory, update cost, duplicates, skew, tail accuracy, and distributed merge limitations.
Statistics & MathTechnical Screen - ExoduspointMediumSoftware EngineerFind minimal time for four people crossing
This question evaluates algorithmic problem-solving and combinatorial optimization skills, focusing on reasoning about constrained pairing, state-space search, and minimizing total time under movement constraints.
Coding & AlgorithmsTechnical Screen - ExpediaHardSoftware EngineerDescribe when you distrusted a colleague
This question evaluates interpersonal judgment, trust assessment, conflict resolution, accountability, and leadership skills in a software engineering context, categorized under Behavioral & Leadership.
Behavioral & LeadershipTechnical Screen - ExpediaHardSoftware EngineerDesign a listing connectivity ingestion service
This question evaluates a candidate's ability to design scalable, reliable REST APIs and distributed ingestion pipelines, testing competencies in API specification, idempotency, authentication/authorization, asynchronous processing, observability, and integration with downstream validation services.
System DesignTechnical Screen - ExpediaHardSoftware EngineerMaximum Sum Skyline
This problem tests your ability to optimize search spaces and use stack-based structures to maintain monotonic properties under specific geometric constraints.
Coding & AlgorithmsTechnical ScreenDynamic ProgrammingData StructuresOptimization - ExpediaHardSoftware EngineerSolve three interview coding problems
This multi-part question evaluates string manipulation, combinatorial counting, and constrained enumeration skills through sequence generation, palindromic-anagram substring counting, and wildcard digit-sum completion tasks.
Coding & AlgorithmsTechnical Screen - ExperianEasyData ScientistCalculate Expected Flips for Two Heads Coin Toss
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Expected Flips for Two Heads Coin Toss states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - ExperianMediumData ScientistCompare Spark RDDs, DataFrames, and SQL Performance Gains
This question evaluates understanding of Spark abstractions (RDDs, DataFrames, Spark SQL), lazy evaluation, memory management, query optimization, and developer ergonomics within the Data Manipulation (SQL/Python) domain of distributed data processing.
Data Manipulation (SQL/Python)Technical Screen - ExperianMediumData ScientistCompare Spark RDDs, DataFrames, and Spark SQL Benefits
This question evaluates understanding of Apache Spark core abstractions (RDDs, DataFrames, Spark SQL), execution model concepts such as lazy evaluation and optimizer-driven execution, and operational competency with managed cloud cluster job submission and monitoring, categorized under Data Manipulation (SQL/Python) for a Data Scientist role.
Data Manipulation (SQL/Python)Technical Screen - ExperianMediumData ScientistDesign Algorithm for Longest Increasing Subsequence Length
This question evaluates algorithm design and data structure skills, covering subsequence analysis, constraint/graph modeling for seating, and external-memory sorting considerations for scalability.
Coding & AlgorithmsTechnical Screen - ExperianMediumData ScientistExplain PCA and L2 Normalization in Machine Learning
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 Explain PCA and L2 Normalization in Machine Learning states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - FaireMediumSoftware EngineerDesign an API for Pascal Triangle
This question evaluates API design, HTTP/web-service principles, data modeling, input validation, error handling, and system constraint reasoning when specifying endpoints, request parameters, response schemas, and practical limits.
Software Engineering FundamentalsTechnical Screen - FaireMediumSoftware EngineerFind first 5-7-5 haiku in sentence
Find first 5-7-5 haiku in sentence evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - FaireMediumSoftware EngineerPrint a Centered Pascal Triangle
This question evaluates the ability to generate combinatorial structures (Pascal's triangle) and apply precise numeric formatting, alignment, and string-layout skills, reflecting algorithmic thinking and attention to presentation details.
Coding & AlgorithmsTechnical Screen - FanaticsMediumSoftware EngineerPlot ASCII chart for time-price pairs
This question evaluates skills in array manipulation, coordinate mapping, and ASCII-based data visualization, focusing on transforming unsorted time-price pairs into a fixed 2D grid and handling point collisions and empty cells.
Coding & AlgorithmsTechnical Screen - Fannie MaeMediumData ScientistAssess SQL joins, unions, windows, dedup, and pandas
This question evaluates proficiency in SQL and pandas data-manipulation techniques, including aggregations with NULL semantics, various join types, set operations and deduplication, window functions, and reconciling normalized and denormalized sources.
Data Manipulation (SQL/Python)Technical Screen - Fetch RewardsMediumData ScientistDesign analysis to validate reported decline
This question evaluates analytical and experimental-design competencies including metric specification, time-series and same-store comparisons, bias and confounder detection, and concise data-driven communication under a tight time constraint.
Analytics & ExperimentationTechnical Screen - Fetch RewardsMediumData ScientistEvaluate Overlapping Shelf Ranking Experiments
This question evaluates competency in experimental design and causal inference for A/B testing, focusing on handling overlapping traffic, interaction effects between concurrent experiments, metric selection, guardrails, and statistical validity.
Analytics & ExperimentationTechnical Screen - Fetch RewardsMediumData ScientistHandle unresponsive stakeholders and align metrics
This question evaluates stakeholder management, metric design and alignment, ambiguity handling, and decision-making communication skills for a data scientist.
Behavioral & LeadershipTechnical Screen - Fetch RewardsMediumData ScientistWrite SQL to verify quarterly sales decline
This question evaluates proficiency in SQL aggregation, time-series period-over-period comparison, same-store cohort analysis, correct grain handling, and treatment of returns, testing data manipulation and analytical SQL skills in the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - FigmaMediumSoftware EngineerAnswer common behavioral questions
This question evaluates a candidate's behavioral and leadership competencies—specifically conflict resolution, influence without formal authority, learning from failure, and making trade-offs—while also testing their ability to quantify impact.
Behavioral & LeadershipTechnical Screen - FigmaMediumSoftware EngineerCheck Graph Reachability For File Permissions
Practice graph reachability for simplified file-permission inheritance under directed, undirected, and blocked-edge variants. The prompt is useful for reviewing DFS or BFS, visited sets, cycle handling, and clarifying traversal semantics.
Coding & AlgorithmsTechnical Screen - FigmaMediumMachine Learning EngineerClarify a BERT Keyphrase Span-Extraction Task
Implement and clarify the post-processing decoder for a BERT-based span-extraction model that maps token-level start and end logits back to non-overlapping character-level keyphrases.
Machine LearningTechnical Screennlpbertspan-extraction - FigmaMediumData ScientistDescribe impact, prioritization, and stakeholder management
Describe impact, prioritization, and stakeholder management 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 & LeadershipTechnical Screen - FigmaHardData ScientistDescribe your most and least engaging work
This question evaluates self-awareness, communication, motivation, collaboration, and role-alignment competencies that reveal which problem types and team environments enable a data scientist to perform best.
Behavioral & LeadershipTechnical Screen - FigmaMediumSoftware EngineerDesign Comments for a Collaborative Whiteboard
Design a real-time, durable commenting system for an infinite collaborative whiteboard where comments attach to dynamic canvas objects or fixed coordinates.
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