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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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- 11
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Showing 4891–4920 of 9403 results
- MetaMediumSoftware EngineerCount Distinct Values in a Sorted Array When K Is Small
Count distinct values in a sorted array efficiently when the number of unique values is much smaller than the array length. The challenge tests adapting complexity to output diversity rather than raw length, meeting an O(k log n) target with constant auxiliary space, and handling empty or uniform arrays.
Coding & AlgorithmsOnsite - MetaMediumSoftware EngineerCount Subarrays with a Target Sum
Tests the ability to use prefix sums and hash tables to count contiguous subarrays summing to a target in linear time, handling negative values and zeros.
Coding & AlgorithmsOnsitearraysprefix-sumhash-table - MetaMediumData ScientistCount heavy callers in 7 days
This question tests the ability to handle temporal windowing and aggregation to identify user behavior patterns based on call activity frequency.
Data Manipulation (SQL/Python)Technical ScreenSQLWindow-FunctionsData-Manipulation - MetaMediumSoftware EngineerCount palindrome substrings in a string
This question evaluates proficiency in string algorithms, algorithmic design, and complexity analysis by targeting palindromic substring counting, string manipulation, and conceptual linear-time techniques.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerCount subarrays equal to target
This problem tests your ability to use a hash map to track prefix sums, allowing for an efficient solution to a subarray counting problem.
Coding & AlgorithmsTechnical ScreenArrayHash MapPrefix Sum - MetaMediumSoftware EngineerCount subarrays summing to target
This question evaluates understanding of prefix-sum concepts, use of linear-time data structures, and competence in handling negative numbers and edge cases such as zero targets and empty prefixes.
Coding & AlgorithmsTechnical Screen - MetaHardSoftware EngineerCount three-digit numbers with distinct digits
This problem tests your ability to count numbers satisfying digit-based constraints within a specific range using combinatorial logic or efficient iteration.
Coding & AlgorithmsOnsiteMathCombinatoricsImplementation - MetaMediumSoftware EngineerDebug and optimize React performance issues
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 Debug and optimize React performance issues states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - MetaMediumMachine Learning EngineerDebug and optimize a card-drawing strategy
This problem tests your ability to simulate card-drawing probabilities, debug flawed logic in iterative selection strategies, and optimize performance for repeated simulations.
Coding & AlgorithmsOnsiteAlgorithmsSimulationProbability - MetaMediumData ScientistDecide and experiment on Group Call feature
This question evaluates a data scientist's proficiency in product analytics, causal inference, metric engineering, experimentation design, and post‑launch diagnostics within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistDecide event notification launch via experiments
This question tests the ability to design a robust A/B test in the presence of network effects, interference, and cannibalization within a social notification ecosystem.
Analytics & ExperimentationTechnical ScreenA/B TestingNetwork EffectsMetrics - MetaMediumSoftware EngineerDecide palindrome within k deletions
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Decide palindrome within k deletions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - MetaHardData ScientistDecide when CTR falls but revenue rises
This question evaluates understanding of A/B experimentation, auction-based ad metrics, metric decomposition for revenue impact, causal diagnostics for hidden heterogeneity (e.g., Simpson’s paradox), and executive-level results presentation within the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - MetaMediumMachine Learning EngineerDeep copy a linked list with random pointers
This problem tests your ability to manipulate complex pointer structures and maintain object identity across a deep copy operation in a non-linear linked list.
Coding & AlgorithmsTechnical ScreenLinked ListHash TablePointers - MetaMediumData ScientistDefine Product Metrics: Align Stakeholders, Measure Success, Improve Results
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Define Product Metrics: Align Stakeholders, Measure Success, Improve Results states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaMediumData ScientistDefine Success for a New Group Feature Without Hiding Cannibalization
Design a comprehensive measurement and experimentation framework for a new community feature, evaluating true incremental value while detecting cannibalization across existing surfaces and mitigating network spillover effects.
Analytics & ExperimentationTechnical Screenexperimentationmetric-designcannibalization - MetaMediumData ScientistDefine and compute shop visibility in SQL
This question evaluates the ability to define and compute shop-level visibility KPIs in SQL, testing skills in metric design (visibility_rate and impression_share), deduplication, session/window filtering, and data-quality concerns such as late-arriving events and bot filtering.
Data Manipulation (SQL/Python)Onsite - MetaHardData EngineerDefine and validate product metrics
Define and validate product metrics evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaEasyData ScientistDefine engagement metrics and analyze comment distribution
This question evaluates a data scientist's competency in metric framework design, distributional analysis of heavy-tailed user behavior, monitoring and anomaly detection, and experimental setup for measuring healthy engagement, including considerations like unit of analysis, treatment of heavy users, and anti-spam guardrails.
Analytics & ExperimentationOnsite - MetaMediumData EngineerDefine metrics and data model for product features
This problem evaluates your ability to translate product goals into actionable metrics and design a scalable, reliable data schema to support those metrics.
System DesignTechnical ScreenData ModelingProduct AnalyticsMetrics - 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 - MetaMediumSoftware EngineerDemonstrate communication and teamwork
This set of behavioral prompts evaluates communication, teamwork, leadership, stakeholder explanation, conflict resolution, and self-reflection skills relevant to a software engineer role.
Behavioral & LeadershipOnsite - MetaHardMachine Learning EngineerDemonstrate leadership and ensure data compliance
This question evaluates leadership, program execution, stakeholder management, risk and tradeoff reasoning in machine learning engineering, along with expertise in data privacy, regulatory compliance, and operational data governance for user-data-driven systems.
Behavioral & LeadershipOnsite - MetaMediumData ScientistDemonstrate leadership in cross-functional collaboration
The Meta Data Scientist onsite behavioral & leadership round: answer a set of STAR-format leadership prompts spanning cross-functional collaboration and conflict, giving/receiving feedback, disagree-and-commit, being wrong and course-correcting, overcoming obstacles and winning skeptics, driving breakthroughs, ambiguous work under time pressure, breaking-metric triage, learning from peers, diversity & inclusion, inclusive meetings, proactive alignment, and building long-term trust. Each answer must name stakeholders, state trade-offs, and quantify impact against a baseline.
Behavioral & LeadershipOnsite - MetaHardData ScientistDeploy multi-armed bandits safely
This question evaluates proficiency in online experimentation and sequential decision-making, covering Bayesian bandits (Thompson Sampling), safety guardrails for churn, delayed-conversion handling, traffic allocation and stopping/rollback policy design.
Machine LearningOnsite - 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 - MetaMediumData ScientistDescribe Overcoming Ambiguity and Building Cross-Team Collaboration
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Overcoming Ambiguity and Building Cross-Team Collaboration states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaEasyData ScientistDescribe a challenging project and work-style conflicts
Meta Data Scientist onsite behavioral & leadership round, anchored on describing a challenging project end-to-end with follow-up prompts on stakeholder influence, collaborating across work styles, mentoring and onboarding teammates, managing cross-functional relationships under disagreement, learning quickly, and quantifying impact and trade-offs. Includes a STAR-based rubric of what interviewers look for at each prompt, common pitfalls, and senior/IC6 expectations.
Behavioral & LeadershipOnsite - MetaMediumProduct AnalystDescribe an Analysis Where You Used AI Responsibly
Prepare a credible story about using an AI-assisted tool in analysis. The exercise tests whether you can justify the tool, validate it independently, protect sensitive data, plan for failure, connect model quality to a product decision, and explain how you build AI fluency.
Behavioral & LeadershipHR Screen
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