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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 4861–4890 of 9403 results
- MetaMediumSoftware EngineerCompute interval mode, BST range sum, exclusive time
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.
Coding & AlgorithmsTechnical Screen - 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 - MetaHardData ScientistCompute p-values, power, and adjust errors
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.
Statistics & MathOnsite - MetaMediumData ScientistCompute posterior and event counts in fraud screen
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.
Statistics & MathOnsite - 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 probabilities for chatbot response quality
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.
Statistics & MathOnsite - 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 - MetaHardSoftware EngineerCompute the Dot Product of Sparse Vectors
Compute the dot product of two sparse vectors stored as sorted index-value pairs without expanding them. A two-pointer traversal should visit only nonzero entries, multiply matching indices, and accumulate safely in a wide integer type.
Coding & AlgorithmsOnsite - MetaMediumMachine Learning EngineerCompute the next lexicographic permutation
This problem tests your ability to manipulate array elements in-place to find the next lexicographically ordered sequence, focusing on understanding the properties of permutations.
Coding & AlgorithmsOnsiteArraysPermutationsTwo Pointers - MetaEasyData ScientistCompute this-year spend share of last-year whales
This question tests the ability to perform cohort analysis and time-based filtering to calculate the contribution of high-value segments across different fiscal years.
Data Manipulation (SQL/Python)OnsiteSQLAggregationCohort Analysis - MetaMediumData ScientistCompute time-spent percentage by app category
This question evaluates proficiency in data manipulation and analytics, specifically SQL/Python skills for joining tables, aggregating session durations, handling missing app-category mappings, and computing percentage time spent over a recent seven-day window.
Data Manipulation (SQL/Python)Onsite - 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 - MetaMediumSoftware EngineerCompute unmatched parentheses length
This problem tests your ability to use stack-based logic to track balanced sequences and identify the length of characters that remain unmatched after processing all valid pairs.
Coding & AlgorithmsOnsiteStringStackGreedy - 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 - MetaMediumData ScientistConstruct a 95% Confidence Interval for Comment Counts
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Construct a 95% Confidence Interval for Comment Counts states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - 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 - MetaMediumProduct ManagerCore Behavioral Reflections
Practice core Product Manager behavioral reflections covering professional failure, proposing an opposed idea, biggest success, and data ethics. The solution emphasizes STAR-L structure, metrics, ownership, influence, privacy, fairness, and learning.
Behavioral & LeadershipOnsite
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