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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 2101–2130 of 3640 results
- MetaMediumSoftware EngineerHandle priority conflicts, setbacks, and initiatives
This question evaluates interpersonal and leadership competencies—communication and influence, structured decision-making and tradeoff reasoning, ownership and accountability, a learning mindset, and the ability to execute through ambiguity and cross-functional constraints.
Behavioral & LeadershipOnsite - MetaMediumProduct ManagerHiking App: Design, Metrics, and Go-to-Market
Practice designing a hiking mobile app with target users, value proposition, MVP features, roadmap, offline maps, safety trade-offs, metrics, instrumentation, launch strategy, partnerships, growth loops, and monetization options.
Product / Decision MakingOnsite - MetaMediumSoftware EngineerHow to answer Staff (L6) behavioral interview questions
These behavioral prompts evaluate leadership competencies including setting technical direction, stakeholder management, influence without formal authority, trade-off analysis, and mentoring at an organization-wide (Staff/L6) level within the Behavioral & Leadership domain for senior software engineers.
Behavioral & LeadershipOnsite - MetaMediumProduct AnalystHow would you drive product growth?
This question evaluates product growth analytics competencies including experimentation design, funnel decomposition, metric definition and guardrails, segmentation, and causal inference within the Analytics & Experimentation domain for social media and mobile product features.
Analytics & ExperimentationOnsite - MetaEasyData ScientistHow would you evaluate emoji reactions launch?
This question tests the ability to design an experimentation framework for a new feature in a social product, specifically focusing on metric selection, identifying interference, and isolating the causal impact of a feature rollout.
Analytics & ExperimentationOnsiteProduct MetricsA/B TestingCausal Inference - MetaEasyProduct AnalystHow would you grow key product metrics?
This question assesses your ability to systematically approach product growth by identifying bottlenecks, framing hypotheses, and designing experiments to improve key user engagement metrics.
Analytics & ExperimentationOnsiteMetricsA/B TestingFunnel Analysis - MetaMediumData ScientistHow would you predict a car’s turning intention?
This question evaluates applied machine learning skills for vehicle intention prediction, covering temporal sequence modeling versus feature engineering, map- and lane-aware representations, multi-agent interaction reasoning, labeling/time-horizon definition, evaluation metrics and calibration, class imbalance and distribution shift handling, and uncertainty estimation for downstream planning. It is commonly asked because intention prediction is safety-critical in autonomous driving and probes a candidate’s ability to make system-level design trade-offs across data labeling, model architecture, training/validation, and risk-aware evaluation. Category/Domain: Machine Learning; Abstraction level: system-level applied ML for data scientist roles (mid-to-senior).
Machine LearningOnsite - MetaMediumProduct ManagerHyperlink Request Flow at Facebook
Practice explaining the end-to-end hyperlink request flow in Facebook, covering mobile client events, routing, DNS, TLS, gateways, auth, GraphQL, caching, rendering, external safe-link handling, performance, security, privacy, and observability.
Product / Decision MakingOnsite - MetaHardData ScientistIdentify Features for Fake News Detection on Facebook
Identify Features for Fake News Detection on Facebook evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - MetaMediumData ScientistIdentify Metrics to Detect Fake-Account Activity on Facebook
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Identify Metrics to Detect Fake-Account Activity on Facebook states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - MetaMediumData ScientistIdentify Top 10 Users by Average Call Duration
This question evaluates SQL-based data manipulation and time-series aggregation skills, specifically computing per-user metrics from timestamped event data such as call durations.
Data Manipulation (SQL/Python)Onsite - MetaMediumSoftware EngineerImpact and Conflict Stories for a Level-Calibrating Behavioral Interview
A behavioral interview asking for a story about your most impactful work and a story about a significant conflict, in a round that weighs heavily in level calibration. It tests scope, ownership, measurable results, handling disagreement constructively, and how well stories hold up under follow-up probing.
Behavioral & LeadershipOnsite - MetaMediumMachine Learning EngineerImplement BST Iterator and Ticket Queue
This problem tests your ability to implement an iterator for a Binary Search Tree (BST) using controlled traversal, and your skill in designing a priority-based queue that handles multi-criteria ordering.
Coding & AlgorithmsOnsiteTreesHeapsIterator - MetaMediumMachine Learning EngineerImplement LRU cache and copy random list
This problem tests your ability to handle complex pointer manipulation through linked-list deep copying and your proficiency in designing a thread-safe-capable cache using hash maps and doubly-linked lists.
Coding & AlgorithmsOnsiteLinked ListsHash MapsLRU Cache - MetaMediumSoftware EngineerImplement basic expression evaluator
This question evaluates proficiency in expression parsing, operator precedence handling, integer arithmetic semantics (including truncating division), and efficient tokenization and evaluation within the Coding & Algorithms domain.
Coding & AlgorithmsOnsite - MetaMediumSoftware EngineerImplement sparse vector dot product
Meta's sparse vector dot product question asks you to design a SparseVector class that stores only non-zero entries and computes dot products in time proportional to the number of non-zeros rather than the full vector length. It tests sparse data representation, two-pointer vs. hash-map trade-offs, and time/space complexity analysis.
Coding & AlgorithmsOnsite - MetaMediumSoftware EngineerImplement weighted random index picker
Implement weighted random index picker 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 & AlgorithmsOnsite - MetaMediumMachine Learning EngineerImplement weighted random sampling with preprocessing
This problem tests your ability to optimize weighted random selection using binary search on prefix sums, balancing preprocessing time against query latency.
Coding & AlgorithmsOnsiteProbabilityBinary SearchPrefix Sum - MetaMediumSoftware EngineerInfer an Unknown Alphabet Order
This question evaluates a candidate's competency in graph-based reasoning, inference of ordering constraints from sequences of strings, and algorithmic manipulation of characters and relations.
Coding & AlgorithmsOnsite - MetaMediumData ScientistInterpreting confidence intervals to choose a treatment
Evaluates confidence-interval interpretation for choosing among feed-ranking treatments with engagement losses. Strong answers compare statistical significance, magnitude, uncertainty, business thresholds, downside risk, and launch recommendations.
Analytics & ExperimentationOnsite - MetaHardData ScientistInvestigate Reasons for Higher Instagram Story Consumption
Evaluates diagnostic analytics for explaining why Instagram users consume more Stories than Facebook users. Strong answers validate metric parity, decompose supply and demand drivers, use cohort and within-user comparisons, and propose experiments for UI, ranking, content, or notification hypotheses.
Analytics & ExperimentationOnsite - MetaHardData ScientistJustify building a new feature with evidence
This question evaluates a data scientist's product analytics, experimental design, and strategic decision-making skills, covering market sizing, competitive analysis, privacy-aware data and infrastructure assessment, risk identification, and A/B testing planning.
Analytics & ExperimentationOnsite - MetaHardData ScientistLead a product deep dive with quantified impact
This question evaluates product leadership, product analytics, experimentation, and impact-measurement competencies for a Data Scientist, emphasizing problem framing, metric definition, trade-off reasoning, risk mitigation, and the ability to quantify and communicate end-to-end results.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerLongest Balanced Substring in a String of Parentheses
A Meta coding interview problem: given a string made only of opening and closing parentheses, return the length of the longest contiguous substring that is correctly balanced. It tests careful handling of matched pairs, unmatched characters that break a run, and efficient processing of long inputs.
Coding & AlgorithmsOnsite - MetaHardMachine Learning EngineerMachine Learning Fundamentals: Optimizers, Scaling Laws, and Clustering
Tests deep conceptual mastery of modern ML core foundations: AdamW vs. Adam weight decay mechanics, compute-optimal Chinchilla scaling laws, and the mathematical equivalence between Hard Expectation-Maximization in GMMs and k-means clustering.
Machine LearningOnsiteOptimizersScaling LawsClustering - MetaMediumData ScientistMaster Behavioral Interview Questions for Data/ML Roles
Master Behavioral Interview Questions for Data/ML Roles evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerMaximize Unique Characters from Words
This question evaluates a candidate's ability to reason about combinatorial constraints and set disjointness while maximizing unique coverage, testing competencies in algorithm design, combinatorics, and constraint-based optimization.
Coding & AlgorithmsOnsite - MetaMediumSoftware EngineerMaximize number with one digit swap
Maximize number with one digit swap 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 & AlgorithmsOnsite - MetaMediumMachine Learning EngineerMaximize one stock trade profit
This question evaluates algorithm design skills for single-pass array processing, streaming data handling, and time/space complexity analysis within the Coding & Algorithms domain.
Coding & AlgorithmsOnsite - MetaMediumSoftware EngineerMaximize score in 15-sum card game
This problem tests your ability to use memoized recursion or dynamic programming to explore state spaces in a combinatorial game where you must decide which cards to remove to maximize your total score.
Coding & AlgorithmsOnsiteRecursionMemoizationBacktracking
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