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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 3211–3240 of 9403 results
- ExpediaHardSoftware EngineerHow do you learn new knowledge quickly?
This question evaluates learning agility, self-directed acquisition of new technologies, and reflective leadership through concrete examples of challenging projects, testing behavioral and leadership competencies for a software engineer role.
Behavioral & LeadershipOnsite - 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 - ExpediaHardData ScientistValidate and monitor ranking model end-to-end
This question evaluates a data scientist's mastery of learning-to-rank concepts, offline evaluation and metric design, position-bias correction, diagnostic analysis, deployment safeguards, and alignment of surrogate objectives with business KPIs for hotel search ranking.
Machine LearningOnsite - 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 - F5NetworksMediumSoftware EngineerFind Top Hashtags
This question evaluates string parsing and tokenization, frequency counting of elements, time-window filtering of events, and the ability to sort results with deterministic tie-breaking rules.
Coding & AlgorithmsOnline Assessment - FaireMediumSoftware EngineerCollaborating With and Helping an ML Engineer as an ML Infrastructure Engineer
A behavioral cross-functional collaboration question for a software engineer on a machine learning infrastructure team, posed by an ML engineer who uses the platform. It tests how you would discover an internal customer's needs, agree on priorities, communicate through changes and failures, and help them succeed.
Behavioral & LeadershipOnsite - FaireMediumMachine Learning EngineerData quality, lineage, monitoring and backfill design for an ML feature store
A discussion-based ML infrastructure design round on keeping the data behind models reliable and well governed. It covers defining and measuring availability, freshness, completeness, validity, consistency, distribution, compliance and lineage, monitoring a feature store, and planning a safe, correct data backfill.
ML System DesignOnsite - FaireMediumSoftware EngineerDesign a product likes system for a marketplace of brands and retailers
Design the backend for product likes on a marketplace where retailers like or dislike products, list the products they liked and see like counts in search results, while brands see the likes on their own products. Tests data modeling for two access directions, counter maintenance, idempotent writes and scaling the search read path.
System DesignOnsite - FaireMediumMachine Learning EngineerDesign a real-time model inference platform: deployment, scaling and monitoring
An ML system design question asking for a real-time model inference platform that teams use to serve trained models inside their applications' request paths. It tests safe model deployment and rollback, per-model autoscaling and batching, failure handling, and monitoring from infrastructure up to model quality.
ML System DesignOnsite - 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 EngineerMax inherited discount and discount-source audit in a category DAG
A two-part coding problem on an e-commerce category hierarchy shaped as a DAG, where a product's applied discount is the largest discount on its category or any ancestor. The follow-up asks for an audit of which discounted categories appear in the ancestry of each bottom-level category, testing traversal order, deduplication and shared work.
Software Engineering FundamentalsOnsite - FaireMediumSoftware EngineerPeak number of bikes needed to cover all pre-booked rental intervals
Given every booking for a bike rental service as a start and end time, compute the peak number of bikes in use at the same moment, which is the smallest fleet that serves all bookings. Tests interval-overlap reasoning, half-open boundary handling and an efficient method for up to 100,000 unsorted bookings.
Coding & AlgorithmsOnsite - 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 - FaireMediumSoftware EngineerValidate assumptions and align stakeholders for a new user-permission feature
An engineer is asked how to validate the assumptions behind a new feature that changes a product's user permission model and how to reach agreement with stakeholders such as security, support and sales. Tests risk-ranked validation, safe migration of existing access, staged rollout, and turning disagreements into clear decisions.
Product / Decision MakingOnsite - FanaticsMediumSoftware EngineerClarify employment type at Microsoft
This question evaluates a candidate's ability to clearly articulate employment arrangements and the degree of ownership, accountability, performance evaluation, and measurable impact on projects, reflecting leadership, communication, and product-stewardship competencies.
Behavioral & LeadershipOnsite - FanaticsHardSoftware EngineerDesign a retail trading platform
This question evaluates a candidate's competencies in designing scalable, low-latency retail trading systems, covering system architecture, order routing and state management, market data ingestion, cash and position ledger consistency, risk controls, and compliance/observability.
System DesignOnsite - 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 - Fannie MaeMediumData ScientistDesign Test Cases for UI Change: Ensuring Quality and Functionality
Evaluates mission-aware QA collaboration for UI and backend changes in a data-science role. Strong answers compare test layers, define acceptance criteria, cover accessibility and auditability, and design robust test cases.
Behavioral & LeadershipOnsite - Fannie MaeMediumData ScientistUnderstand SQL Aggregations and Joins: Key Differences Explained
This question evaluates a candidate's competency in SQL aggregations (COUNT, SUM, AVG, MIN, MAX), join semantics (INNER, LEFT, RIGHT, FULL OUTER), set operations (UNION vs UNION ALL), window functions, view versus table trade-offs, duplicate detection and removal, query optimization techniques, and pandas DataFrame operations (merge, join, concat) within the Data Manipulation (SQL/Python) domain. It is commonly asked because it assesses both conceptual understanding (function purposes and join semantics) and practical application (writing efficient queries, deduplicating data, and translating patterns between SQL and pandas), thereby probing correctness and performance reasoning.
Data Manipulation (SQL/Python)Onsite - 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
Expedia
Experian
Faire
Fetch Rewards