Meta interview questions

Showing 91–120 of 666 results

• Compute Max Score From Up to 3 CategoriesHard

  This Coding & Algorithms question in the Data Engineer domain evaluates grouping and top‑k aggregation concepts, testing the ability to combine per-category aggregation with selection under a uniqueness constraint.

  MetaData EngineerCoding & AlgorithmsTechnical Screen
• Compute Range Sum QuicklyEasy

  This question tests the ability to trade preprocessing time for query performance using prefix sum arrays to efficiently calculate the sum of elements within a specified range.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute SHOP spend share and model performanceMedium

  This question evaluates proficiency in SQL/Python data manipulation and analytics, focusing on aggregations, joins, time-window computations, handling missing or zero values, and deriving key metrics such as revenue share and revenue-per-conversion.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute Shop Visibility Rate Using SQL and PythonMedium

  This question evaluates proficiency in data manipulation and metric design using SQL and Python, focusing on event-level aggregation, user deduplication, and definition of a business visibility metric within the Data Manipulation (SQL/Python) domain.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Compute Sliding Window AveragesEasy

  This question tests the ability to implement a sliding window algorithm to compute the average of contiguous subarrays, emphasizing optimization of time complexity.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute Stock Profit with One or Unlimited TransactionsMedium

  Compute maximum stock profit for both one transaction and unlimited nonoverlapping transactions in linear time, returning zero when no positive trade is available.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Compute active ad revenue by creation sourceEasy

  This question evaluates proficiency in data manipulation and analytics, testing skills such as joining event and impression tables, aggregating time-series revenue, and counting distinct active ads broken down by creation source.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Compute ad impression conversion ratesMedium

  This question evaluates proficiency in time-based event attribution, aggregation of business metrics (impressions, conversion rates, spend and revenue), timestamp handling, and user/ad-level matching within the Data Manipulation (SQL/Python) domain, emphasizing practical application over abstract theory.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Compute ad revenue metrics by geography in SQLMedium

  In the Data Manipulation (SQL/Python) domain, this intermediate-level question evaluates SQL aggregation, join logic between impressions and clicks, date-based UTC attribution, user-geo joins, and null/divide-by-zero-safe computation of derived metrics (CTR, RPM) at the country-day granularity.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute an Overnight Alarm IntervalEasy

  This problem tests the ability to calculate time intervals across date rollovers by converting 12-hour clock times into a continuous 24-hour linear timeline.

  MetaSoftware EngineerStatistics & MathHR Screen
• Compute and correct correlation significance inflationMedium

  This question tests the understanding of how multiple hypothesis testing increases the risk of false positives (Type I error) in high-dimensional correlation matrices and requires the application of correction methods to maintain statistical integrity.

  MetaData ScientistStatistics & MathTechnical Screen
• Compute binary tree diameterMedium

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compute binary tree diameter states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute binary-tree diameter via return-only DFSMedium

  This question evaluates algorithmic problem-solving with binary trees, focusing on tree traversal techniques, recursion versus iterative implementations, and analysis of time and space complexity including worst-case input shapes such as skewed trees.

  MetaData ScientistCoding & AlgorithmsTechnical Screen
• Compute capacities after site closuresMedium

  This question evaluates a candidate's ability to manipulate nested dictionary data structures and perform aggregate capacity computations, assessing data transformation, mapping, and set-based reasoning skills relevant to a Data Engineer within the Coding & Algorithms domain.

  MetaData EngineerCoding & AlgorithmsTechnical Screen
• Compute cohort GMV and payer rate with edge casesMedium

  This question evaluates a data scientist's competency in cohort-based GMV calculation, payer-rate measurement, and handling common edge cases across events, orders, payments, and refunds.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute daily active adsEasy

  This question tests your ability to aggregate time-series event data and perform distinct counts based on state transitions.

  MetaAnalytics EngineerData Manipulation (SQL/Python)Onsite
• Compute fraud probabilities with Bayes and BinomialMedium

  This question evaluates a candidate's understanding of probabilistic modeling and statistical decision-making, focusing on the Binomial distribution for session-level events and Bayes' theorem for posterior probabilities in a fraud-detection setting.

  MetaData ScientistStatistics & MathOnsite
• Compute interval mode, BST range sum, exclusive timeMedium

  This multipart question evaluates proficiency in interval coverage and frequency analysis, binary search tree range-sum queries with preprocessing for repeated queries, and exclusive-time computation from call logs, covering concepts such as event counting/sweep-line, BST traversal and augmentation, and nested-call time accounting.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute left and right views onceMedium

  This question evaluates understanding of binary tree traversal, depth tracking, and view computation for left and right perspectives, plus the ability to implement a concurrent single-pass traversal while handling edge cases such as empty trees and duplicate keys.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute longest increasing path in a matrixMedium

  Compute longest increasing path in a matrix evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute longest rising path in a gridMedium

  This question evaluates mastery of graph traversal, dynamic programming, and complexity analysis for computing longest increasing paths in a matrix, focusing on algorithmic correctness and efficient use of time and space.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute max profit from single stock tradeMedium

  This question evaluates a candidate's ability to design efficient array algorithms, reason about time and space complexity constraints, and handle edge cases related to single-transaction profit calculation.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute multi-account user distribution and unread pctEasy

  This question evaluates a data scientist's ability to perform relational data manipulation and aggregation with SQL or Python, covering competencies such as handling one-to-many joins, grouping and bucketing counts, deduplicating across related tables, and computing cohort-level percentages from boolean flags.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute nested depth-weighted sumMedium

  This question evaluates understanding of tree-like nested data structures, traversal strategies (recursive DFS and iterative approaches), handling of edge cases such as empty lists and negative integers, and algorithmic reasoning about time and space complexity within the Coding & Algorithms domain.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute p-values, power, and adjust errorsHard

  This question evaluates statistical reasoning and applied inference skills including hypothesis testing for proportions with sequential monitoring, multiple-testing control (Benjamini–Hochberg vs Bonferroni), Bayesian posterior estimation for classification flags, and robust AOV estimation and transformation techniques.

  MetaData ScientistStatistics & MathOnsite
• Compute posterior and event counts in fraud screenMedium

  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.

  MetaData ScientistStatistics & MathOnsite
• Compute posterior fake probability using Bayes' ruleMedium

  The problem evaluates understanding of Bayes' rule and conditional probability within the Statistics & Math domain for a Data Scientist role, at an introductory applied probability/interpretation level.

  MetaData ScientistStatistics & MathTechnical Screen
• Compute probabilities for chatbot response qualityEasy

  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.

  MetaData ScientistStatistics & MathOnsite
• Compute probability an account is fakeEasy

  This question tests the ability to apply Bayes' Theorem to update the probability of a hypothesis (an account being fake) given new evidence (an automated flag).

  MetaData ScientistStatistics & MathTechnical Screen
• Compute reservation diff for largest memberMedium

  This question evaluates competency in relational data manipulation and aggregation using SQL or Python, including joins, grouping, and handling missing referrer relationships.

  MetaData EngineerData Manipulation (SQL/Python)Technical Screen