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 4921–4950 of 9403 results
- MetaEasySoftware EngineerDescribe background and job motivations
This question evaluates communication, self-awareness, motivation, and cultural-fit competencies within the Behavioral & Leadership domain for a Software Engineer role.
Behavioral & LeadershipHR Screen - MetaMediumSoftware EngineerDescribe conflict resolution and initiative
This question evaluates conflict resolution, initiative, ownership, influence, and learning from failure within a Behavioral & Leadership context for a software engineer.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerDescribe conflict resolution and leadership
This question evaluates conflict resolution, leadership, cross-team collaboration, stakeholder management, and senior-level ownership and communication competencies in the Behavioral & Leadership category within the software engineering domain.
Behavioral & LeadershipTechnical Screen - MetaHardSoftware EngineerDescribe conflict resolution and mentoring experiences
Prepare engineering manager or senior engineer behavioral answers for conflict resolution, proud projects, self-reflection, and mentoring. Includes STAR structures, sample story patterns, and pitfalls.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerDescribe cross-team collaboration and learning from failure
This question evaluates a software engineer's proficiency in cross-team collaboration, conflict and constraint management, ownership alignment, and reflective learning from project failure, emphasizing interpersonal, leadership, and accountability competencies.
Behavioral & LeadershipOnsite - MetaMediumMachine Learning EngineerDescribe handling intense time pressure
This question evaluates a candidate's ability to perform under intense time pressure and multitask, specifically assessing prioritization, trade-off communication, stakeholder management, stress resilience, and execution skills relevant to machine learning engineering within a Behavioral & Leadership context.
Behavioral & LeadershipOnsite - MetaMediumData ScientistDescribe influencing without authority
This question evaluates a candidate's ability to influence without formal authority, covering stakeholder management, cross-functional communication, conflict resolution, and technical trade-off quantification within a Behavioral & Leadership interview for a Data Scientist role.
Behavioral & LeadershipOnsite - MetaMediumData ScientistDescribe leadership and collaboration examples
The question evaluates a Data Scientist's leadership, cross-functional collaboration, stakeholder communication, conflict resolution, onboarding, and impact-measurement competencies within a product analytics context.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerDescribe proudest project and cross-team work
This question evaluates leadership, cross-team collaboration, ownership, and reflective learning by asking about a candidate's proudest project and a specific instance of working across teams, and it falls under the Behavioral & Leadership category within the software engineering domain.
Behavioral & LeadershipOnline Assessment - MetaMediumSoftware EngineerDescribe resolving conflict by persuading others
This question evaluates persuasion, conflict-resolution, communication, and stakeholder-management competencies and falls within the Behavioral & Leadership domain.
Behavioral & LeadershipOnsite - MetaEasyData ScientistDescribe something you did to be inclusive
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe something you did to be inclusive states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerDescribe teamwork and stress handling
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe teamwork and stress handling states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnline Assessment - MetaMediumSoftware EngineerDesign 24-Hour Ephemeral Stories for a Photo and Video Sharing App
A system design question on ephemeral stories that followers can view for 24 hours before they disappear, covering media upload, the tray of accounts with unwatched stories, playback, and viewer tracking. It tests expiry semantics, fan-out for popular authors, watched-state storage, and media delivery.
System DesignOnsite - MetaHardData ScientistDesign A/B test and success metrics for new feature
This question evaluates a data scientist's competency in experimental design, metric selection and guardrails, power and sample-size calculations, logging and attribution specifications, and revenue impact estimation for a new feature on a social media platform.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDesign B2C chatbot success metrics and test plan
This question evaluates a data scientist's ability to design product success metrics and a rigorous experiment plan, covering KPI formulation, guardrail metrics and thresholds, randomization and experiment design, logging instrumentation, and quasi-experimental causal inference.
Analytics & ExperimentationOnsite - MetaHardSoftware EngineerDesign Boolean Search for Status Posts
This problem tests the ability to design an inverted index system that supports complex logical queries, high-concurrency updates, and consistent pagination over a distributed dataset.
System DesignOnsiteInverted IndexSearchDistributed Systems - MetaMediumSoftware EngineerDesign Boolean Search over User Status Posts
This problem tests the design of a scalable inverted index system capable of handling boolean queries (AND/OR/NOT) over a high-throughput stream of user status updates.
System DesignOnsiteInverted IndexBoolean SearchConcurrency - MetaHardData ScientistDesign Experiment to Measure Shopping Feature Impact
Meta data scientist experimentation prompt on measuring Instagram Shopping impact with A/B tests, geo or cluster holdouts, engagement and revenue metrics, guardrails, selection bias, network effects, and rollout decisions.
Analytics & ExperimentationOnsite - MetaMediumSoftware EngineerDesign LCA and find K closest points
This question evaluates understanding of tree algorithms (LCA using parent pointers) and selection algorithms (sorting, max-heap, quickselect), emphasizing data structures, algorithmic correctness, and time/space complexity within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - MetaHardData ScientistDesign Machine Learning Model for Facebook Groups Post Ranking
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 Machine Learning Model for Facebook Groups Post Ranking states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - MetaHardData ScientistDesign Messenger spam experiment with clustering
This question evaluates a data scientist's skills in experimental design, causal inference, clustering and interference analysis, metric specification, and power/sample-size calculation within the Analytics & Experimentation domain for Data Scientist roles.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistDesign Metrics to Track and Analyze Spam Impact
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 Metrics to Track and Analyze Spam Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaHardMachine Learning EngineerDesign Nearby and Notification Ranking
This question evaluates competency in designing large-scale machine learning systems for real-time recommendations and notification ranking, covering candidate generation, ranking, feature engineering, online serving, latency management, and multi-objective trade-offs; it is commonly asked to assess the ability to translate product objectives into system architecture and measurable evaluation metrics while addressing freshness, cold-start, moving users, and notification fatigue. The domain is ML system design—recommender systems, ranking, and online serving—and the level spans both high-level conceptual architecture and practical application concerns such as serving latency, data freshness, training labels, and evaluation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign Place Recommendation System
This question evaluates machine learning system design skills specific to recommender systems, including competencies in feature engineering, candidate generation and ranking, data engineering, online serving, latency and real-time context handling, evaluation metrics, and fairness/bias mitigation.
ML System DesignOnsite - MetaMediumSoftware EngineerDesign Real-Time Auctions for Social Posts
This question evaluates skills in designing scalable, low-latency real-time systems, including distributed architecture, concurrency control and ordering, data modeling, API design, and fault-tolerance for high-contention auction workloads.
System DesignOnsite - MetaHardSoftware EngineerDesign Ride Requests, Driver Matching, and Trip State
Design a high-scale real-time ride-hailing dispatch and trip lifecycle backend. The problem tests geospatial indexing, high-throughput location ingestion, concurrent driver assignment without double-booking, and idempotent distributed state management.
System DesignOnsiteDistributed SystemsGeospatialConcurrency - MetaMediumData ScientistDesign SQL Query for Shop Visibility and User Activity Metrics
This question evaluates proficiency in SQL-based data manipulation—aggregation, JOINs, CASE expressions, window functions, ranking, and metric design for user tenure segmentation and temporal activity comparisons.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistDesign Sampling Strategy to Estimate Fake News Proportion
Design Sampling Strategy to Estimate Fake News Proportion 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 - MetaMediumSoftware EngineerDesign Search And Web Crawling Systems
This question tests the ability to design a scalable web crawler capable of handling billions of pages while maintaining freshness and avoiding redundant work.
System DesignTechnical ScreenDistributed SystemsWeb CrawlingScalability - MetaMediumSoftware EngineerDesign a Code Review Agent
This question evaluates a candidate's ability to design ML-powered developer tooling, covering competencies in LLM orchestration, deterministic tool integration, repository context management, code-analysis heuristics, safety and validation strategies, and inference cost estimation.
ML System DesignOnsite
Meta