Meta interview questions

Showing 1–30 of 666 results

• Add Two Arbitrarily Long Non-Negative Integers Given as Decimal StringsMedium

  Add two non-negative integers that are given as decimal digit strings, possibly thousands of digits long, and return the sum as a string without converting to built-in numbers. Tests digit-by-digit addition with carries, inputs of unequal length and canonical output without leading zeros.

  MetaSoftware EngineerCoding & AlgorithmsOther
• Analyze Algorithm's Impact on Diverse Demographics and Validate CausesHard

  Analyze Algorithm's Impact on Diverse Demographics and Validate Causes 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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Analyze Bookstore Data to Identify Top Payment MethodsMedium

  This question evaluates data manipulation and analytical SQL/Python competency, focusing on relational joins, aggregations, string filtering, ranking, and proportion calculations across multiple tables in a bookstore schema.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze Change in App Metrics and Feature ImpactHard

  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 Analyze Change in App Metrics and Feature Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Analyze Comment Distribution Using Statistical Metrics and TestsMedium

  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 Analyze Comment Distribution Using Statistical Metrics and Tests states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Analyze Hashtag Follow Behavior with SQL QueriesMedium

  This question evaluates a candidate's competency in data manipulation and analytical SQL techniques—specifically aggregation, filtering, joins, and calculation of proportions—to analyze hashtag follow behavior.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Analyze Key Metrics for Notification System SuccessMedium

  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 Analyze Key Metrics for Notification System Success states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Analyze Minimum Completion Time for Target Courses in a Prerequisite DAGHard

  Analyze the minimum time needed to finish target courses and only their transitive prerequisites in a DAG. Distinguish total study effort from elapsed time with parallel courses, count shared prerequisites once, exclude unrelated nodes, and detect reachable cycles.

  MetaSoftware EngineerSoftware Engineering FundamentalsOnsite
• Analyze Mobile Promo Orders with SQL Query and MetricsMedium

  This question evaluates SQL data-manipulation and analytical competencies, including filtered joins, conditional aggregation, date-window filtering, and business-metric design to measure promotional performance on mobile orders.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze Oculus App Engagement with SQL QueriesMedium

  This question evaluates proficiency in SQL-based data manipulation and aggregation, time-window filtering, percentage calculations, and the application of basic statistical hypothesis testing for comparing user engagement across categories.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Analyze Revenue Shifts to Identify Cannibalization EffectsMedium

  Evaluates revenue cannibalization analysis across creation sources. Strong answers define Source A, other-source revenue, total revenue, cannibalization ratio, and net lift, then use panel, rollout, difference-in-differences, or synthetic-control evidence to separate source shifting from organic growth.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Analyze Seller Activity and Vehicle Listing InteractionsMedium

  This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Analyze Seller Activity and Vehicle Listing Interactions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze Social Media Engagement with SQL QueriesMedium

  This question evaluates SQL data-manipulation and analytics competencies, including aggregation, DISTINCT counting, joins between view and action logs, date-range filtering, and conditional metric computation from event data.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze Thirty-Day Ad Performance with SQLMedium

  This problem tests proficiency in window functions, time-based aggregation, and join logic to analyze multi-dimensional advertising performance metrics.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Analyze Top Call Initiators and Active French Video CallersMedium

  This question evaluates proficiency in SQL-based data manipulation and analytics—specifically aggregation, grouping, time-window filtering, and percentage calculations—by asking for the top call initiators over the last seven days and the proportion of active users in France on video calls yesterday, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role with a focus on practical application. It is commonly asked in technical interviews to assess a data scientist's ability to extract actionable engagement metrics from event logs, reason about temporal windows and population denominators, and produce accurate, explainable reporting from production-style datasets.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze User Comment Distribution and Sampling EffectsMedium

  Evaluates statistical reasoning about right-skewed user comment counts and sampling distributions. Strong answers place median, mean, and p95 correctly, then use the Central Limit Theorem to explain why group averages become less variable and more symmetric.

  MetaData ScientistStatistics & MathOnsite
• Analyze User Engagement and Spammer Read-Rate in SQLMedium

  This question evaluates SQL-based data manipulation and analytics skills—specifically joins, time-window filtering, aggregation, and metric calculation for measuring user engagement and spammer read-rate—within the Data Manipulation (SQL/Python) domain.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze User Transfer Distribution in Initial Launch PeriodMedium

  Evaluates distribution analysis for transfer counts after a new P2P payments launch. Strong answers specify the denominator, describe zero inflation and right skew, locate mean, median, mode, and p95, and explain how adoption, cohorts, and exposure change the distribution over time.

  MetaData ScientistStatistics & MathOnsite
• Analyze ad targeting expectations and distributionsMedium

  This question evaluates a data scientist's competency in probabilistic modeling, expected-value decision-making, variance decomposition for mixture distributions, and Poisson/exponential arrival-process analysis within the Statistics & Math domain.

  MetaData ScientistStatistics & MathHR Screen
• Analyze skewed comments and sampling effectsMedium

  This question evaluates understanding of descriptive statistics for right‑skewed count data and inferential concepts such as sampling distributions, standard error, percentiles, and the Central Limit Theorem, and is commonly asked to probe how skewness and sample size affect central tendency measures and uncertainty in estimates.

  MetaData ScientistStatistics & MathOnsite
• Analyzing abuse in the content‑reporting systemMedium

  Evaluates reporting-system analytics for valid reports and misuse detection in moderation workflows. Strong answers define distinct reported-user share, write SQL for valid reports, and detect abusive reporters through frequency, hit-rate, burst, and coordination metrics.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Annotating and forecasting a long‑tail distributionMedium

  Evaluates robust summarization and forecasting for long-tailed daily share-count distributions. Strong answers compute mean, median, P1, P99, visualize with log scales or percentile bands, and forecast seasonality with heavy-tail-aware methods.

  MetaData ScientistStatistics & MathOnsite
• Answer Repeated Range Aggregate Queries on a Static BSTHard

  Preprocess a sorted array derived from a static BST to answer many inclusive range queries. Use binary-search boundaries and wide prefix sums to return both the sum and count for each range without rescanning the tree.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Answer behavioral questions on projects and feedbackMedium

  This set of behavioral questions evaluates communication, leadership, adaptability, handling ambiguity, feedback exchange, and project ownership within the context of machine learning engineering projects.

  MetaMachine Learning EngineerBehavioral & LeadershipOnsite
• Answer conflict, ambiguity, feedback, difficult coworker promptsMedium

  This question evaluates a candidate's ability to handle conflict with supervisors, clarify ambiguous requirements, provide constructive feedback and mentorship, and manage difficult collaborators.

  MetaSoftware EngineerBehavioral & LeadershipTechnical Screen
• Answer core behavioral questions using STARMedium

  This question evaluates behavioral and leadership competencies—including communication, teamwork, conflict resolution, time and scope management, and mentorship—within the context of a Machine Learning Engineer role.

  MetaMachine Learning EngineerBehavioral & LeadershipOnsite
• Answer four string/array/battery coding questionsMedium

  This set of four problems evaluates algorithmic competencies including ASCII string processing and character classification, array transformation and operation-count reasoning, cost-minimization for enforcing monotonicity under only-increase constraints, and time-based simulation of resource scheduling with recharge cycles.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Answer key behavioral prompts effectivelyHard

  This set of behavioral prompts evaluates ownership, communication, conflict resolution, adaptability, and the ability to receive and act on feedback, targeting leadership and interpersonal competencies relevant to software engineering.

  MetaSoftware EngineerBehavioral & LeadershipOnsite
• Answer senior-level behavioral interview questionsMedium

  This question evaluates leadership, decision-making, risk management, stakeholder management, and impact-communication skills for a senior Machine Learning Engineer role within the Behavioral & Leadership category.

  MetaMachine Learning EngineerBehavioral & LeadershipTechnical Screen
• Apply reinforcement learning to product decisionsMedium

  This question assesses the ability to determine the appropriate RL framework for recommendation systems, evaluate policies using offline data, and implement safety guardrails.

  MetaData ScientistMachine LearningOnsite