Meta interview questions

Showing 61–90 of 666 results

• Canonicalize an Absolute Unix-Style Path With Dot and Double-Dot SegmentsMedium

  Convert an absolute Unix-style file path into its canonical form by collapsing repeated slashes, dropping single-dot segments, resolving double-dot segments against the parent directory, and treating other dot sequences as ordinary names. It tests careful string parsing and edge cases such as the root directory.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Characterize and compare transfer-count distributions over timeMedium

  This question evaluates a candidate's ability to model zero-inflated, heavy-tailed count data, interpret distributional summaries and percentiles, reason about temporal shifts from retention and fraud dynamics, and identify robust summary statistics and diagnostics for a Data Scientist role.

  MetaData ScientistStatistics & MathOnsite
• Characterize metric distribution and quantilesMedium

  This question evaluates a data scientist's competency in descriptive statistics and robust metric analysis within the Statistics & Math domain, focusing on distribution characterization, empirical quantiles, measures of central tendency, and the effects of outlier handling on variance and experimental power.

  MetaData ScientistStatistics & MathOnsite
• Check Whether Deleting at Most K Characters Can Form a PalindromeHard

  Determine whether a string can be transformed into a palindrome by removing at most k characters. Tests dynamic programming, palindrome properties, and recursive search with branch bounding.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Check Whether a Letters-and-Numbers Abbreviation Matches a WordEasy

  Decide whether an abbreviation made of lowercase letters and decimal skip counts matches a given word, where each number skips that many characters and any number with a leading zero is invalid. It tests careful string scanning, multi-digit number parsing and end-of-input boundary checks.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Check diagonal and compute window statisticsMedium

  This question evaluates understanding of array and matrix manipulation, sliding-window and streaming statistics, and appropriate data-structure choices for maintaining aggregates and order-statistics such as moving averages and sliding-window medians.

  MetaMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Check diagonal equality in a matrixMedium

  This question evaluates skill in matrix manipulation and algorithmic reasoning, specifically understanding diagonal traversal, equality checks, and handling boundary conditions such as empty matrices, single-row or single-column inputs, and negative values.

  MetaMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Children’s Bookshelf Product DesignMedium

  Practice a children's bookshelf product design case covering target users, caregiver and child pain points, safety principles, ergonomic design, MVP scope, intentional exclusions, validation methods, reading engagement metrics, and quality guardrails.

  MetaProduct ManagerProduct / Decision MakingOnsite
• Choose alternatives when randomization failsHard

  This question evaluates proficiency in causal inference and experimentation analytics for a Data Scientist role, focusing on selecting appropriate identification strategies, designing placebo and pre‑trend tests, and specifying robustness and sensitivity analyses for non‑randomized feature adoption.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Choose and compute recommender evaluation metricsHard

  This question evaluates competency in offline recommender-system evaluation, propensity-weighted policy comparison, classification metric interpretation, probability calibration, model selection trade-offs, and handling data leakage and network interference.

  MetaData ScientistMachine LearningHR Screen
• Choose group-call size cap via experimentHard

  This question evaluates a candidate's competency in experimental design, A/B testing and statistical analysis for product decisions, specifically testing metric selection, reliability and latency guardrails, stratified randomization, interference and clustering effects, heterogeneity detection, and operational stopping rules.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Choose the Cheapest Round TripMedium

  This problem tests the ability to optimize a two-step decision process by maintaining track of the best previous state, requiring a linear scan approach.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Choose the Longest Set of Words with No Repeated CharactersHard

  Tests bitmask representation, backtracking with pruning, and combinatorial optimization under character-uniqueness constraints.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Choose threshold under asymmetric costsMedium

  This question evaluates cost-sensitive classification, operating-threshold selection, score calibration, drift monitoring, and experiment and guardrail design competencies within the Machine Learning domain.

  MetaData ScientistMachine LearningOnsite
• Clarify scope and align to missionHard

  This question evaluates a data scientist's product sense, leadership and stakeholder-alignment skills, including scope clarification, mission translation, metric definition, and collaborative communication.

  MetaData ScientistBehavioral & LeadershipTechnical Screen
• Clone list with random pointersMedium

  Meta software-engineer technical-screen coding question: deep-copy a singly linked list whose nodes carry an arbitrary random pointer (which may be null, point backward, or form cycles). It tests both the O(n)-space hash-map solution and the O(1)-auxiliary-space in-place weaving technique, plus reasoning about pointer integrity, cycle safety, and verifying the copy is structurally equal to and isolated from the original.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Combine Many Sorted Integer Arrays into One Non-Decreasing ArrayMedium

  A coding question asking you to merge many integer arrays, each already sorted in non-decreasing order, into a single sorted array that keeps every duplicate value. It tests multi-way merging, efficiency as the number of input arrays grows, and edge cases such as empty inputs and arrays of different lengths.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Communicate trade-offs and influence launchHard

  This question evaluates product judgment, experimentation interpretation, and cross-functional leadership skills for a data scientist, focusing on communicating trade-offs between per-account metric lifts and cross-account cannibalization; Category: Behavioral & Leadership, Domain: data science and product experimentation.

  MetaData ScientistBehavioral & LeadershipTechnical Screen
• Compare Ad-Insertion Strategies: Expected Ads and ProbabilitiesEasy

  Evaluates probability, expectation, and variance for newsfeed ad-insertion strategies. Strong answers compute expected ad load, at-least-one exposure probability, and explain why equal expected counts can still produce different user experiences.

  MetaData ScientistStatistics & MathOnsite
• Compare Bayesian and frequentist decisionsMedium

  This question evaluates understanding of Bayesian inference versus frequentist hypothesis testing, decision theory under asymmetric loss, sequential monitoring/optional stopping, and prior elicitation applied to A/B testing within the Statistics & Math domain.

  MetaData ScientistStatistics & MathOnsite
• Compare Instagram and Facebook Stories Using Key Performance MetricsMedium

  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 Compare Instagram and Facebook Stories Using Key Performance Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Compare Instagram vs. Facebook using causal experimentsMedium

  This question evaluates causal inference, experimentation design, metric engineering, and ecosystem-level impact assessment skills for a Data Scientist in the Analytics & Experimentation domain, with emphasis on defining a single-objective OEC that prevents pure cannibalization and on measuring cross-app effects via randomized rollouts.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Compare Survey Satisfaction for New and Established UsersMedium

  Tests your ability to write PostgreSQL queries that segment users into cohorts based on date logic, filter invalid survey events, and calculate aggregate satisfaction metrics.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute 7-day views and reactions by relationshipMedium

  This question evaluates skills in SQL-based data manipulation—specifically deduplication by longest duration, join-based attribution of reactions to views, window functions, distinct counts, aggregations, and date-range filtering within the Data Manipulation (SQL/Python) domain.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute BST range sumMedium

  This question evaluates understanding of binary search tree properties, traversal techniques, and the ability to implement both recursive and iterative algorithms while reasoning about time and space complexity.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Compute Bayes probability for fake accountsEasy

  This question tests the ability to apply Bayes' Theorem in a practical classification scenario, specifically accounting for low base-rate bias and conditional probabilities.

  MetaData ScientistStatistics & MathOnsite
• Compute CTR overall and by campaign typeMedium

  This question evaluates data manipulation and analytics skills, specifically SQL aggregations, deduplication of event logs, time-window filtering, joins to campaign metadata, and safe ratio computation including division-by-zero handling.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute Each Advertiser's Share of Shop Ad SpendMedium

  This problem tests your ability to perform windowed aggregations, conditional filtering, and percentage calculations across multiple tables using SQL.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Compute Exclusive Execution TimesMedium

  This problem tests your ability to manage state in nested processes using a stack data structure to track the exclusive execution time of functions based on a log of start and end events.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute High-Call Usage RatesHard

  This question evaluates a candidate's ability to perform advanced data manipulation and analytical SQL tasks, including joins, aggregations, timezone-aware date handling, rolling window metrics, distinct user counting, and counting user participations across roles.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen