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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 5161–5190 of 9403 results
- MetaMediumData ScientistFind the most-used app
This question evaluates data manipulation and metrics-definition skills, emphasizing SQL and Python proficiency for aggregating user activity and joining app metadata within analytics workflows.
Data Manipulation (SQL/Python)Onsite - MetaMediumSoftware EngineerFind top-K frequent elements in a stream
This question evaluates understanding of streaming algorithms, frequency estimation, and data-structure-based top-K selection, emphasizing space/time and accuracy trade-offs when processing large or unbounded data.
Coding & AlgorithmsOnsite - MetaMediumMachine Learning EngineerGenerate unique permutations with duplicates
This question evaluates a candidate's understanding of permutation generation and duplicate handling within algorithm design, testing combinatorial reasoning, correctness proofs, and time/space complexity analysis.
Coding & AlgorithmsOnsite - MetaMediumProduct ManagerGoals, North-Star Metric and Guardrails for Scam Protection Across Meta's Apps
A metrics question asking a product manager to set goals and success metrics for a scam-prevention product across Meta's apps. It tests goal prioritization, the choice and measurement of a north-star metric, leading indicators by scam type and surface, guardrails against over-enforcement, and measurement under adversarial behavior.
Analytics & ExperimentationOther - MetaHardProduct ManagerGoogle–Roomba Acquisition Strategy
Practice a Google and Roomba acquisition strategy case with smart-home fit, user value, privacy governance, competitive landscape, financial modeling, regulatory risk, integration planning, alternatives, and a clear go or no-go recommendation.
Product / Decision MakingOnsite - MetaMediumSoftware EngineerGroup Binary Tree Nodes by Vertical Column, Top to Bottom and Left to Right
Return a binary tree's node values grouped by vertical column from left to right, ordering each column top to bottom and breaking ties within a row by left-to-right position. The tree arrives as a level-order list, and the task tests coordinate tracking during traversal and precise tie handling.
Coding & AlgorithmsOnsite - MetaMediumSoftware EngineerGroup a tree by vertical columns
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Group a tree by vertical columns states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - MetaHardSoftware EngineerGuide a mouse to find cheese with APIs
This problem tests your ability to navigate an unknown environment by maintaining internal state and implementing an efficient search algorithm to reach a target.
Coding & AlgorithmsTechnical ScreenGraphDFSBacktracking - MetaMediumSoftware EngineerHandle conflict and missed delivery deadlines
This question evaluates interpersonal competencies such as conflict resolution, relationship management, communication, ownership, and accountability in scenarios involving missed delivery deadlines and team dynamics.
Behavioral & LeadershipOnsite - MetaEasyAnalytics EngineerHandle diverse styles and give constructive feedback
This question evaluates an Analytics Engineer's competencies in collaboration, inclusive communication, conflict resolution, constructive feedback delivery, and documentation practices within the Behavioral & Leadership domain.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerHandle feedback and priority conflicts
Handle feedback and priority conflicts 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 & LeadershipOnsite - MetaEasyData ScientistHandle feedback, change pivots, and conflict
A Meta Data Scientist technical-screen behavioral question covering three leadership prompts: giving or receiving constructive feedback, pivoting when a project's direction or business conditions change, and resolving conflict with a teammate or stakeholder. The model answer gives a STAR rubric, strong vs. weak signals, and a practical template for each story.
Behavioral & LeadershipTechnical Screen - MetaMediumSoftware EngineerHandle palindrome & decimal addition
This question evaluates string manipulation and numeric representation skills, specifically handling decimal-point addition with arbitrary precision and reasoning about palindrome/permutation properties.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerHandle priority conflicts, setbacks, and initiatives
This question evaluates interpersonal and leadership competencies—communication and influence, structured decision-making and tradeoff reasoning, ownership and accountability, a learning mindset, and the ability to execute through ambiguity and cross-functional constraints.
Behavioral & LeadershipOnsite - MetaMediumData ScientistHandle sales pressure with analytical integrity
This question evaluates a data scientist's skills in analytical integrity, causal inference, experimental design, stakeholder communication, and change management when pressured to convert a correlation into policy.
Behavioral & LeadershipTechnical Screen - MetaMediumProduct ManagerHiking App: Design, Metrics, and Go-to-Market
Practice designing a hiking mobile app with target users, value proposition, MVP features, roadmap, offline maps, safety trade-offs, metrics, instrumentation, launch strategy, partnerships, growth loops, and monetization options.
Product / Decision MakingOnsite - MetaMediumData ScientistHow investigate a brand-ad spend drop?
This question evaluates a data scientist's competency in diagnosing ad-spend anomalies, including data validation, attribution and reporting integrity, segmentation and placement effects, and causal inference within advertising analytics.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistHow to Validate Friends' Content Engagement Hypothesis?
Evaluates how to validate whether friends' content drives stronger social engagement than unconnected content. Strong answers define socialness metrics, control observational bias, test launch impact for unconnected feed content, and monitor discovery, retention, cannibalization, and quality guardrails.
Analytics & ExperimentationTechnical Screen - MetaMediumSoftware EngineerHow to answer Staff (L6) behavioral interview questions
These behavioral prompts evaluate leadership competencies including setting technical direction, stakeholder management, influence without formal authority, trade-off analysis, and mentoring at an organization-wide (Staff/L6) level within the Behavioral & Leadership domain for senior software engineers.
Behavioral & LeadershipOnsite - MetaHardData ScientistHow to design Shop ad ranking
This problem tests your ability to design a multi-objective ranking system for e-commerce ads, balancing short-term engagement, advertiser conversion, and platform revenue while mitigating feedback loops.
Machine LearningTechnical ScreenAd RankingMulti-task LearningRecommender Systems - MetaEasyData ScientistHow to evaluate a similar-listing notifications feature
A Meta Data Scientist analytics-and-experimentation interview question: a US C2C second-hand marketplace is considering opt-in “similar listings” notifications. You must (1) decide whether to build it — hypotheses, opportunity sizing, risks, MVP plan — and (2) design the post-launch measurement: a user-randomized A/B test with a primary/diagnostic/guardrail metric stack, handling of opt-in selection bias, and a clear launch/rollback rule.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistHow to evaluate new listing notifications?
This problem tests your ability to design an A/B test for a marketplace notification feature, focusing on balancing buyer engagement with potential cannibalization and ecosystem health metrics.
Analytics & ExperimentationTechnical ScreenA/B TestingMetricsMarketplace - MetaEasyData ScientistHow would you define and use retention metrics?
This question evaluates a data scientist's competency in product analytics and experimentation by assessing understanding of retention metric definitions, cohorting strategies, short- versus long-term trade-offs, common measurement pitfalls, and A/B test design considerations.
Analytics & ExperimentationTechnical Screen - MetaEasyData ScientistHow would you design a Shop Ads ranking algorithm?
This problem tests the ability to design an end-to-end ad ranking system, focusing on objective optimization, feature engineering, and mitigating common biases in marketplace environments.
Machine LearningTechnical ScreenSystem DesignRankingAds - MetaMediumProduct AnalystHow would you drive product growth?
This question evaluates product growth analytics competencies including experimentation design, funnel decomposition, metric definition and guardrails, segmentation, and causal inference within the Analytics & Experimentation domain for social media and mobile product features.
Analytics & ExperimentationOnsite - MetaEasyData ScientistHow would you evaluate emoji reactions launch?
This question tests the ability to design an experimentation framework for a new feature in a social product, specifically focusing on metric selection, identifying interference, and isolating the causal impact of a feature rollout.
Analytics & ExperimentationOnsiteProduct MetricsA/B TestingCausal Inference - MetaEasyData ScientistHow would you evaluate pixel-issue notifications?
This question tests the ability to build a comprehensive evaluation framework for a new feature, specifically focusing on defining success metrics, identifying potential side effects, and designing an experimental setup for pixel-issue notifications.
Analytics & ExperimentationTechnical ScreenMetric DesignA/B TestingCausal Inference - MetaEasyData ScientistHow would you evaluate upranking shop ads?
This question tests your ability to design an A/B testing framework for evaluating ad ranking changes, including metric selection, segment analysis, and identifying potential biases in historical data.
Analytics & ExperimentationTechnical ScreenA/B TestingMetricsAdTech - MetaEasyProduct AnalystHow would you grow key product metrics?
This question assesses your ability to systematically approach product growth by identifying bottlenecks, framing hypotheses, and designing experiments to improve key user engagement metrics.
Analytics & ExperimentationOnsiteMetricsA/B TestingFunnel Analysis - MetaMediumData ScientistHow would you measure Group Call success?
This question evaluates a data scientist's ability to design a measurement framework encompassing product goal definition, north-star metric selection, success metrics for adoption, engagement and quality, precise retention definitions (app-level vs feature-specific and creator vs participant), cohort retention windows, and experimental design including guardrails, bias sources and network effects. It is asked in the Analytics & Experimentation domain to assess practical application of product analytics and quantitative reasoning about short-term versus long-term tradeoffs, segmentation and experimental validity, operating at a primarily practical application level with necessary conceptual understanding.
Analytics & ExperimentationTechnical Screen
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