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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
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- 546
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Showing 2371–2400 of 4616 results
- MetaHardData ScientistDeliver an elevator pitch and impact example
This question evaluates a data scientist's communication and leadership competencies, including concise elevator pitching, product sense, end-to-end experimentation design and statistical rigor, causal reasoning, and the ability to quantify measurable business impact within a product context.
Behavioral & LeadershipTechnical Screen - MetaMediumMachine Learning EngineerDerive Linear Regression Solution
This problem tests your ability to derive the Ordinary Least Squares (OLS) closed-form solution for a simple linear regression model and analyze the underlying mathematical assumptions.
Machine LearningTechnical ScreenLinear RegressionMathematicsMLE - MetaMediumSoftware EngineerDerive max distinct frequencies for n items
The question evaluates combinatorial reasoning, discrete mathematics, and constructive extremal analysis by asking for the maximum number of distinct frequency counts in an array and a matching construction.
Statistics & MathTechnical Screen - 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 - 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 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 - 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 - 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 Concurrent, Memory-Bounded Tally Service
This problem tests the ability to design a high-throughput, memory-efficient data structure for time-series counting that balances concurrency, accuracy, and storage constraints.
Software Engineering FundamentalsTechnical ScreenConcurrencyTime-SeriesData-Structures - MetaMediumMachine Learning EngineerDesign a Real-Time Question-Answering Chatbot: Training Data, RL Tasks and Rewards
An AI design question about a chatbot that answers user questions in real time. It tests the low-latency serving and retrieval path, training data collection for fine-tuning and preference learning, reinforcement-learning task and reward design, and the ability to name reward weaknesses and mitigate them.
ML System DesignTechnical Screen - MetaMediumMachine Learning EngineerDesign a Short-Video Recommendation System
This question tests your ability to design a multi-stage recommendation pipeline for a high-traffic short-video platform, focusing on candidate retrieval, ranking models, and system scalability.
ML System DesignTechnical ScreenRecommendation SystemsScalabilityRanking - MetaEasySoftware EngineerDesign a String Calculator with Explicit Grammar and Evaluation Rules
This problem tests the ability to implement a parser for basic mathematical expressions, specifically focusing on handling operator precedence and parentheses using a stack-based approach.
Software Engineering FundamentalsTechnical ScreenStacksParsingAlgorithms - MetaEasySoftware EngineerDesign a Trade Ledger Class
This problem evaluates your ability to design a class interface that maintains an ordered collection of events and allows efficient retrieval based on time-based criteria.
Software Engineering FundamentalsTechnical ScreenData StructuresAPI DesignBinary Search - MetaHardSoftware EngineerDesign a bank system with scheduling and rankings
Design a bank system with scheduling and rankings 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 DesignTechnical Screen - MetaMediumData ScientistDesign a clustered notification experiment with guardrails
This question evaluates experimental-design and causal-inference competencies for cluster-randomized experiments, covering metric selection, power and design-effect calculations, intracluster correlation, spillover detection, sequential monitoring, guardrails, and operational rollout within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to determine how applicants balance statistical rigor and real-world operational constraints—such as cluster choice, monitoring thresholds, and analysis strategy—testing both conceptual understanding and practical application in production experimentation.
Analytics & ExperimentationTechnical Screen - MetaMediumSoftware EngineerDesign a mini banking system
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Design a mini banking system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - MetaMediumSoftware EngineerDesign a ranking and recommendation system
This problem tests your ability to design a multi-stage recommendation pipeline that balances low-latency retrieval with high-precision ranking at scale.
System DesignTechnical ScreenMachine LearningRecommendation SystemsScalability - MetaMediumSoftware EngineerDesign a real-time ad impression aggregator
This question evaluates a candidate's ability in scalable real-time system design, covering competencies such as stream processing, event aggregation, time-windowed metrics, handling late and out-of-order events, and designing for very high write throughput.
System DesignTechnical Screen - MetaHardSoftware EngineerDesign a scalable banking system
This problem tests your ability to design a distributed, high-concurrency ledger system that maintains strict consistency and atomicity for financial transactions.
System DesignTechnical ScreenDistributed SystemsACIDDatabase Design - MetaMediumMachine Learning EngineerDesign a weapon-ad harmful content detection system
This question assesses the ability to architect a scalable, multi-modal machine learning pipeline for detecting policy-violating content (weapon advertisements) within a high-throughput ad-delivery system.
System DesignTechnical ScreenMulti-modal MLComputer VisionNLP - MetaHardData ScientistDesign an A/B Test for Group Video Calls 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 an A/B Test for Group Video Calls Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDesign an A/B test for pinned-unread feature
This question evaluates proficiency in experiment design and causal inference for product analytics, including choosing the randomization unit, defining eligibility and exposure, selecting primary and guardrail metrics, performing power/MDE and sample-size analysis, and addressing identity cross-contamination and network effects.
Analytics & ExperimentationTechnical Screen - MetaHardSoftware EngineerDesign an Automated Ticket Investigation Agent
This question evaluates a candidate's ability to design AI-enabled agentic systems for automated ticket investigation, covering competencies in ticket understanding, classification and prioritization, multi-source evidence retrieval (logs, metrics, traces, documentation), root-cause diagnosis, remediation orchestration, and human-in-the-loop safety controls. It is commonly asked in the ML system design domain to assess architectural and integration thinking, data retrieval and evaluation strategies, production monitoring and safety considerations, and tests both high-level conceptual architecture and practical application-level implementation concerns.
ML System DesignTechnical Screen - MetaHardSoftware EngineerDesign an Online Game Leaderboard
A Meta software-engineer system-design screen: design the backend for an online game leaderboard supporting score recording, a global top-N board, a friends board, a player's rank and nearby players, and daily/weekly/seasonal/all-time windows at high write volume and low read latency. The model answer separates a durable score event log from an in-memory sorted ranking store, computes the friends board on read, and covers tie-breaking, caching, sharding, consistency trade-offs, anti-cheat, and rebuilds.
System DesignTechnical Screen - MetaHardData ScientistDesign an experiment to evaluate a new ads algorithm
This question evaluates a data scientist's competency in experimental design, causal inference, metric selection and trade-offs, randomization and interference reasoning, rollout strategies, and observational methods for algorithm evaluation, including consideration of stakeholder impacts such as users, advertisers, and revenue.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDesign analytics and experiment for group video calls
This question evaluates product analytics and experimentation competencies, including tie-strength and group-affinity measurement, event-level instrumentation design, proxy-product forecasting, participant-cap constraints and operational trade-offs, and experiment design that accounts for network effects and contamination.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistDesign and analyze a group-calls experiment
This question evaluates competency in experimental design, causal inference, metric specification, interference handling, and infrastructure-aware analytics within the Analytics & Experimentation domain for a Data Scientist role.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDesign cluster-randomized test under network effects
This question evaluates a data scientist's experiment design and causal inference skills under interference, covering exposure and estimand definition, graph-based cluster construction, cluster-level randomization and contamination mitigation, metric selection, and design-effect/sample-size calculations.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDesign experiment for Group Calls with interference
This question evaluates a data scientist's competency in experimental design and causal inference for networked features, covering metrics selection (primary and guardrail metrics), strategies for mitigating interference via randomization or quasi-experimental approaches, exposure and contamination controls, rollout planning, and pre-registration and power analysis. Commonly asked in Analytics & Experimentation interviews for Data Scientist roles, it assesses reasoning about bias–variance trade-offs, engineering constraints, and validity diagnostics in interconnected user environments, requiring both conceptual understanding of identification and practical application of experiment implementation.
Analytics & ExperimentationTechnical Screen
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