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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 1921–1950 of 3640 results
- MetaMediumData ScientistCompute posterior and event counts in fraud screen
This question evaluates a candidate's competence in probability and statistical inference, covering binomial probability calculations, Bayes' theorem for posterior probability, rare-event base-rate reasoning, and modeling of correlated binary signals in a fraud-screening context.
Statistics & MathOnsite - MetaEasyData ScientistCompute probabilities for chatbot response quality
This question evaluates a candidate's grasp of basic probability concepts, including independence, joint event probabilities, and i.i.d. assumptions. It is commonly asked to test probabilistic reasoning about repeated trials within the Statistics & Math domain and is primarily a conceptual understanding problem with a simple practical application.
Statistics & MathOnsite - MetaHardSoftware EngineerCompute the Dot Product of Sparse Vectors
Compute the dot product of two sparse vectors stored as sorted index-value pairs without expanding them. A two-pointer traversal should visit only nonzero entries, multiply matching indices, and accumulate safely in a wide integer type.
Coding & AlgorithmsOnsite - MetaMediumMachine Learning EngineerCompute the next lexicographic permutation
This problem tests your ability to manipulate array elements in-place to find the next lexicographically ordered sequence, focusing on understanding the properties of permutations.
Coding & AlgorithmsOnsiteArraysPermutationsTwo Pointers - MetaEasyData ScientistCompute this-year spend share of last-year whales
This question tests the ability to perform cohort analysis and time-based filtering to calculate the contribution of high-value segments across different fiscal years.
Data Manipulation (SQL/Python)OnsiteSQLAggregationCohort Analysis - MetaMediumData ScientistCompute time-spent percentage by app category
This question evaluates proficiency in data manipulation and analytics, specifically SQL/Python skills for joining tables, aggregating session durations, handling missing app-category mappings, and computing percentage time spent over a recent seven-day window.
Data Manipulation (SQL/Python)Onsite - MetaMediumSoftware EngineerCompute unmatched parentheses length
This problem tests your ability to use stack-based logic to track balanced sequences and identify the length of characters that remain unmatched after processing all valid pairs.
Coding & AlgorithmsOnsiteStringStackGreedy - MetaMediumData ScientistConstruct a 95% Confidence Interval for Comment Counts
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Construct a 95% Confidence Interval for Comment Counts states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - MetaMediumProduct ManagerCore Behavioral Reflections
Practice core Product Manager behavioral reflections covering professional failure, proposing an opposed idea, biggest success, and data ethics. The solution emphasizes STAR-L structure, metrics, ownership, influence, privacy, fairness, and learning.
Behavioral & LeadershipOnsite - 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 - 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 - 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 - 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 - 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 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 - 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 - 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 - 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 - 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
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