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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.
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Showing 4831–4860 of 9403 results
- MetaHardData ScientistCommunicate trade-offs and influence launch
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.
Behavioral & LeadershipTechnical Screen - MetaEasyData ScientistCompare Ad-Insertion Strategies: Expected Ads and Probabilities
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.
Statistics & MathOnsite - MetaMediumData ScientistCompare Bayesian and frequentist decisions
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.
Statistics & MathOnsite - MetaMediumData ScientistCompare Instagram and Facebook Stories Using Key Performance Metrics
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.
Analytics & ExperimentationOnsite - MetaMediumData ScientistCompare Instagram vs. Facebook using causal experiments
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.
Analytics & ExperimentationOnsite - MetaMediumData ScientistCompare Survey Satisfaction for New and Established Users
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.
Data Manipulation (SQL/Python)Technical Screensqlpostgresqldata-aggregation - MetaMediumData ScientistCompute 7-day views and reactions by relationship
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.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumSoftware EngineerCompute BST range sum
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.
Coding & AlgorithmsOnsite - MetaEasyData ScientistCompute Bayes probability for fake accounts
This question tests the ability to apply Bayes' Theorem in a practical classification scenario, specifically accounting for low base-rate bias and conditional probabilities.
Statistics & MathOnsiteBayes TheoremProbabilityStatistics - MetaMediumData ScientistCompute CTR overall and by campaign type
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.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistCompute Each Advertiser's Share of Shop Ad Spend
This problem tests your ability to perform windowed aggregations, conditional filtering, and percentage calculations across multiple tables using SQL.
Data Manipulation (SQL/Python)OnsiteSQLAggregationWindow Functions - MetaMediumSoftware EngineerCompute Exclusive Execution Times
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.
Coding & AlgorithmsTechnical ScreenStackString ParsingSimulation - MetaHardData ScientistCompute High-Call Usage Rates
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.
Data Manipulation (SQL/Python)Technical Screen - MetaHardData EngineerCompute Max Score From Up to 3 Categories
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.
Coding & AlgorithmsTechnical Screen - MetaEasySoftware EngineerCompute Range Sum Quickly
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.
Coding & AlgorithmsTechnical ScreenPrefix SumArraysOptimization - MetaMediumData ScientistCompute SHOP spend share and model performance
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.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistCompute Shop Visibility Rate Using SQL and Python
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.
Data Manipulation (SQL/Python)Onsite - MetaEasySoftware EngineerCompute Sliding Window Averages
This question tests the ability to implement a sliding window algorithm to compute the average of contiguous subarrays, emphasizing optimization of time complexity.
Coding & AlgorithmsTechnical ScreenSliding WindowArraysOptimization - MetaMediumSoftware EngineerCompute Stock Profit with One or Unlimited Transactions
Compute maximum stock profit for both one transaction and unlimited nonoverlapping transactions in linear time, returning zero when no positive trade is available.
Coding & AlgorithmsOnsite - MetaEasyData ScientistCompute active ad revenue by creation source
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.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistCompute ad impression conversion rates
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.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistCompute ad revenue metrics by geography in SQL
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.
Data Manipulation (SQL/Python)Technical Screen - MetaEasySoftware EngineerCompute an Overnight Alarm Interval
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.
Statistics & MathHR ScreenMathDate-TimeAlgorithms - MetaMediumData ScientistCompute and correct correlation significance inflation
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.
Statistics & MathTechnical ScreenStatisticsHypothesis TestingCorrelation - MetaMediumSoftware EngineerCompute binary tree diameter
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.
Coding & AlgorithmsTechnical Screen - MetaMediumData ScientistCompute binary-tree diameter via return-only DFS
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.
Coding & AlgorithmsTechnical Screen - MetaMediumData EngineerCompute capacities after site closures
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.
Coding & AlgorithmsTechnical Screen - MetaMediumData ScientistCompute cohort GMV and payer rate with edge cases
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.
Data Manipulation (SQL/Python)Technical Screen - MetaEasyAnalytics EngineerCompute daily active ads
This question tests your ability to aggregate time-series event data and perform distinct counts based on state transitions.
Data Manipulation (SQL/Python)OnsiteSQLAggregationData Manipulation - MetaMediumData ScientistCompute fraud probabilities with Bayes and Binomial
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.
Statistics & MathOnsite
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