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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
- Companies
- 11
- Topics
- 8
- Languages
Showing 2431–2460 of 4616 results
- MetaMediumData ScientistEvaluate a Live-Stream Group Notification Under Network Effects
This question tests the ability to design an experiment that accounts for spillover effects and user fatigue in a social graph, while proposing causal inference methods for environments where randomization is restricted.
Analytics & ExperimentationTechnical ScreenCausal InferenceNetwork EffectsExperimentation - MetaMediumSoftware EngineerEvaluate expression without stack, constant space
Evaluate expression without stack, constant space 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 - MetaHardData ScientistEvaluate shopping tab pre- and post-launch
This question evaluates a data scientist's competency in causal inference, experiment design, funnel metric instrumentation, revenue modeling, sensitivity analysis, and data‑quality triage within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistEvaluating a 15 % reduction in post‑card height
Evaluates feed UX experimentation for reducing post-card height and diagnosing divergent geographic revenue outcomes. Strong answers measure scroll efficiency, viewability, engagement, ad revenue, device/network effects, and geo-specific decisions.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistEvaluating the Facebook ‘Memory’ feature
Evaluates Facebook Memories feature performance beyond click-through rate and surface-level engagement. Strong answers measure incremental engagement, retention, cannibalization, quality guardrails, frequency caps, sensitive-content handling, and launch criteria.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistEvaluating the Impact of Duplicate and Stolen Posts on a Content Platform
This question tests the ability to design a measurement framework for evaluating content-quality problems on a large user-generated-content platform. It assesses competency in metric selection, experimentation design, and causal reasoning within the Analytics & Experimentation domain — skills central to data science roles focused on platform health.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistExplain Your Motivation for Career Transition and Role Interest
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Explain Your Motivation for Career Transition and Role Interest states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - MetaEasyProduct AnalystExplain a project’s impact and product thinking
This question evaluates a product analyst's product thinking, analytical rigor, experiment design and execution, metric selection and trade-off analysis, and ability to quantify and communicate project impact while demonstrating leadership in end-to-end ownership.
Behavioral & LeadershipTechnical Screen - MetaMediumSoftware EngineerExplain a recent project and measured impact
This question evaluates project leadership, technical decision-making, trade-off analysis, cross-functional influence, and the ability to quantify impact with metrics.
Behavioral & LeadershipTechnical Screen - MetaMediumSoftware EngineerExplain an AI System You Designed and the Problem It Solved
Tests your ability to articulate an end-to-end production AI/ML system you designed, detailing the business justification, technical architecture, evaluation strategy, failure modes, and your individual contributions.
ML System DesignTechnical Screenml-system-designai-architecturemodel-evaluation - MetaMediumSoftware EngineerExtend a Maze Solver
This question evaluates algorithmic problem-solving and software engineering skills, specifically graph traversal (BFS), state-space modeling for keys and doors, handling movement constraints, and debugging an existing codebase within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerFill rooms with nearest gate distances
This question evaluates grid-based graph traversal and algorithmic problem-solving skills, including concepts such as multi-source breadth-first search, handling sentinel values, and performing in-place updates.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerFind K-th Largest and Longest Vacation
This pair of problems evaluates array manipulation and algorithmic problem-solving skills, specifically order-statistics for selecting the k-th largest element and maximizing consecutive sequences under constrained transformations for the vacation streak problem.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerFind Kth Largest and Tree Ancestors
This question evaluates algorithmic problem-solving and data-structure competencies, focusing on selection algorithms and time/space complexity reasoning for the k-th largest element and tree traversal, lowest common ancestor discovery with attention to BST properties, parent pointers, and external-memory constraints.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerFind Shortest Unique Prefixes
This problem tests your ability to efficiently manage and query prefix-based string sets using a Trie (prefix tree) data structure.
Coding & AlgorithmsTechnical ScreenTrieStringsData Structures - MetaMediumMachine Learning EngineerFind a String Containing Another
This question evaluates proficiency in string algorithms, substring search techniques, and algorithmic complexity analysis within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerFind balanced subarray and increasing tree path
This question evaluates array and tree algorithm skills, testing competency in identifying longest balanced subarrays in binary sequences and locating maximal strictly increasing downward paths in binary trees.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerFind k most frequent elements, discuss O(n)
Find k most frequent elements, discuss O(n) 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 EngineerFind minimum and most frequent number efficiently
Find minimum and most frequent number efficiently 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 EngineerFind mode or minimum with time-space tradeoffs
This question evaluates algorithm design and analysis skills—particularly time–space tradeoffs, frequency counting techniques (sorting, hashing, and counting arrays), and handling streaming or memory-constrained inputs in the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerFind power-of-two subarrays within sum range
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Find power-of-two subarrays within sum range states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerFind the kth largest element
This question evaluates a candidate's mastery of finding the k-th largest element using selection algorithms and heap data structures, along with algorithmic time and space complexity analysis and techniques for adapting solutions to streaming inputs.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerGroup a tree by vertical columns
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Group a tree by vertical columns states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - MetaHardSoftware EngineerGuide a mouse to find cheese with APIs
This problem tests your ability to navigate an unknown environment by maintaining internal state and implementing an efficient search algorithm to reach a target.
Coding & AlgorithmsTechnical ScreenGraphDFSBacktracking - MetaEasyData ScientistHandle feedback, change pivots, and conflict
A Meta Data Scientist technical-screen behavioral question covering three leadership prompts: giving or receiving constructive feedback, pivoting when a project's direction or business conditions change, and resolving conflict with a teammate or stakeholder. The model answer gives a STAR rubric, strong vs. weak signals, and a practical template for each story.
Behavioral & LeadershipTechnical Screen - MetaMediumSoftware EngineerHandle palindrome & decimal addition
This question evaluates string manipulation and numeric representation skills, specifically handling decimal-point addition with arbitrary precision and reasoning about palindrome/permutation properties.
Coding & AlgorithmsTechnical Screen - MetaMediumData ScientistHandle sales pressure with analytical integrity
This question evaluates a data scientist's skills in analytical integrity, causal inference, experimental design, stakeholder communication, and change management when pressured to convert a correlation into policy.
Behavioral & LeadershipTechnical Screen - MetaMediumData ScientistHow investigate a brand-ad spend drop?
This question evaluates a data scientist's competency in diagnosing ad-spend anomalies, including data validation, attribution and reporting integrity, segmentation and placement effects, and causal inference within advertising analytics.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistHow to Validate Friends' Content Engagement Hypothesis?
Evaluates how to validate whether friends' content drives stronger social engagement than unconnected content. Strong answers define socialness metrics, control observational bias, test launch impact for unconnected feed content, and monitor discovery, retention, cannibalization, and quality guardrails.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistHow to design Shop ad ranking
This problem tests your ability to design a multi-objective ranking system for e-commerce ads, balancing short-term engagement, advertiser conversion, and platform revenue while mitigating feedback loops.
Machine LearningTechnical ScreenAd RankingMulti-task LearningRecommender Systems
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