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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
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- 11
- Topics
- 8
- Languages
Showing 2341–2370 of 4616 results
- MetaMediumSoftware EngineerCompute left and right views once
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.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerCompute longest increasing path in a matrix
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.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerCompute longest rising path in a grid
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.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerCompute max profit from single stock trade
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.
Coding & AlgorithmsTechnical Screen - MetaEasyData ScientistCompute multi-account user distribution and unread pct
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.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumSoftware EngineerCompute nested depth-weighted sum
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.
Coding & AlgorithmsTechnical Screen - MetaMediumData ScientistCompute posterior fake probability using Bayes' rule
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.
Statistics & MathTechnical Screen - MetaEasyData ScientistCompute probability an account is fake
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).
Statistics & MathTechnical ScreenBayesian StatisticsConditional ProbabilityClassification Metrics - MetaMediumData EngineerCompute reservation diff for largest member
This question evaluates competency in relational data manipulation and aggregation using SQL or Python, including joins, grouping, and handling missing referrer relationships.
Data Manipulation (SQL/Python)Technical Screen - MetaHardData ScientistCompute sample size and test duration correctly
This question evaluates competency in experiment design and statistical power calculations, including sample size estimation, variance misspecification effects, multiple-comparison corrections, sequential monitoring boundaries, and adjustments for covariates or clustering.
Statistics & MathTechnical Screen - MetaMediumData ScientistCompute shop visibility and intent metrics in SQL
This question evaluates SQL-based data manipulation and analytical competencies in the Data Manipulation (SQL/Python) domain, focusing on deduplication, time-zone-aware timestamp handling, window functions, aggregation, and construction of visibility, CTR, and intent metrics for shops.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumMachine Learning EngineerCompute sliding-window medians
This question evaluates proficiency in designing and analyzing efficient sliding-window algorithms and data structures for computing running medians, including correct handling of duplicates, deletions, and the convention for defining the median when k is even.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerCompute sparse dot product and count islands
This question evaluates competency in designing efficient algorithms for sparse data representations and for identifying connected components in grids, testing skills in handling large inputs, memory-efficient representations, and traversal/graph concepts.
Coding & AlgorithmsTechnical Screen - MetaMediumData ScientistCompute unconnected 60s posts and reactions averages
This question evaluates data manipulation skills in SQL and Python, focusing on deduplication, join logic between event and reaction tables, date handling for string-typed dates, and aggregation to compute per-post metrics.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistCompute unread and multi-account user percentages
This question evaluates understanding of SQL data manipulation skills such as aggregations, joins, NULL handling, deduplication reasoning, and user- versus person-level computations for accurate percentage metrics.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumSoftware EngineerCompute vertical order of a BST
This question evaluates understanding of binary tree traversals and coordinate-based ordering, including tie-breaking by breadth-first visitation order, plus competency in algorithmic time and space complexity analysis and strategies for handling deep recursion and streaming large inputs.
Coding & AlgorithmsTechnical Screen - MetaMediumData ScientistCompute view prevalence from views and labels
This question evaluates a candidate's competency in temporal joins, stateful deduplication of moderation decisions, time-aware aggregation, and prevalence metric calculations within content-moderation analytics.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumSoftware EngineerCompute weighted sum of nested lists
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 weighted sum of nested lists states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerConvert BST to sorted doubly list
This question evaluates understanding of binary search tree properties, in-order traversal, in-place pointer manipulation, and algorithmic complexity when converting tree nodes into a sorted doubly linked list.
Coding & AlgorithmsTechnical Screen - MetaMediumData ScientistCount heavy callers in 7 days
This question tests the ability to handle temporal windowing and aggregation to identify user behavior patterns based on call activity frequency.
Data Manipulation (SQL/Python)Technical ScreenSQLWindow-FunctionsData-Manipulation - MetaMediumSoftware EngineerCount palindrome substrings in a string
This question evaluates proficiency in string algorithms, algorithmic design, and complexity analysis by targeting palindromic substring counting, string manipulation, and conceptual linear-time techniques.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerCount subarrays equal to target
This problem tests your ability to use a hash map to track prefix sums, allowing for an efficient solution to a subarray counting problem.
Coding & AlgorithmsTechnical ScreenArrayHash MapPrefix Sum - MetaMediumSoftware EngineerCount subarrays summing to target
This question evaluates understanding of prefix-sum concepts, use of linear-time data structures, and competence in handling negative numbers and edge cases such as zero targets and empty prefixes.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerDebug and optimize React performance issues
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Debug and optimize React performance issues states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - MetaMediumData ScientistDecide and experiment on Group Call feature
This question evaluates a data scientist's proficiency in product analytics, causal inference, metric engineering, experimentation design, and post‑launch diagnostics within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistDecide event notification launch via experiments
This question tests the ability to design a robust A/B test in the presence of network effects, interference, and cannibalization within a social notification ecosystem.
Analytics & ExperimentationTechnical ScreenA/B TestingNetwork EffectsMetrics - MetaMediumSoftware EngineerDecide palindrome within k deletions
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Decide palindrome within k deletions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - MetaMediumMachine Learning EngineerDeep copy a linked list with random pointers
This problem tests your ability to manipulate complex pointer structures and maintain object identity across a deep copy operation in a non-linear linked list.
Coding & AlgorithmsTechnical ScreenLinked ListHash TablePointers - MetaMediumData ScientistDefine Success for a New Group Feature Without Hiding Cannibalization
Design a comprehensive measurement and experimentation framework for a new community feature, evaluating true incremental value while detecting cannibalization across existing surfaces and mitigating network spillover effects.
Analytics & ExperimentationTechnical Screenexperimentationmetric-designcannibalization - MetaMediumData EngineerDefine metrics and data model for product features
This problem evaluates your ability to translate product goals into actionable metrics and design a scalable, reliable data schema to support those metrics.
System DesignTechnical ScreenData ModelingProduct AnalyticsMetrics
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