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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 5191–5220 of 9403 results
- 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 - MetaMediumData ScientistIdentify 3-Person Call Cycles in Video-Calling App
This question evaluates SQL data-manipulation competency, including aggregation, distinct counting, date filtering and relational pattern detection (three-node cycles) to measure user engagement and group-call signals.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistIdentify Algorithms for Detecting Malicious Duplicated Content
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Identify Algorithms for Detecting Malicious Duplicated Content states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - 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 - MetaHardData ScientistIdentify non-table data for feature demand
The question evaluates a data scientist's competency in triangulating product demand from non-table sources, defining and de-biasing signals, specifying metrics and guardrails, and designing evidence-driven experiments within the Analytics & Experimentation domain for a Data Scientist role.
Analytics & ExperimentationTechnical Screen - 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 - MetaHardMachine Learning EngineerImplement 1NN Embeddings and Forward Pass
Implement a fully vectorized forward pass that projects input features into an embedding space and classifies them using a 1-nearest-neighbor lookup against a reference bank.
Machine LearningTechnical Screennumpydeep-learninglinear-algebra - 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 K-Means Clustering From Scratch
An ML coding question that asks for a from-scratch implementation of k-means clustering with no provided test cases. It tests the assignment and update steps, vectorized distance computation, initialization, stopping rules, empty-cluster handling and the ability to verify code with self-constructed checks.
Machine LearningTechnical Screen - 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 - MetaEasySoftware EngineerImplement Multi-Level In-Memory Services
This question evaluates a candidate's ability to design and implement mutable in-memory data structures and algorithms for stateful service simulations, covering account and file modeling, temporal operations with lazy expiration, ranking queries, merging entities, and capacity constraints.
Coding & AlgorithmsOnline Assessment - MetaMediumMachine Learning EngineerImplement Sparse Matrix Operations
This question tests the ability to design an efficient data structure for sparse matrix storage and implement optimized multiplication between a sparse matrix and a dense vector.
Coding & AlgorithmsTechnical ScreenData StructuresLinear AlgebraOptimization - MetaMediumSite Reliability EngineerImplement a Streaming VMStat Alert
This question tests the ability to design a memory-efficient sliding window mechanism for monitoring high-frequency system metrics in real-time.
Coding & AlgorithmsTechnical ScreenSliding WindowData StructuresStreaming - MetaHardSoftware EngineerImplement a Timestamped Banking System
This problem tests the ability to manage time-series data using efficient lookups and range queries, specifically requiring a balance between transactional updates and historical state retrieval.
Coding & AlgorithmsOnline AssessmentData StructuresBinary SearchTime-series - MetaMediumSoftware EngineerImplement a versioned in-memory DB with CAS and history
This question evaluates a candidate's ability to design and implement time-versioned in-memory data structures, conditional concurrency primitives (compare-and-set and compare-and-delete), TTL expiration semantics, and efficient historical reads.
Coding & AlgorithmsOnline Assessment - MetaMediumSoftware EngineerImplement an Expiring Record Store
This question evaluates skills in designing and implementing an in-memory, level-based key-field-value store with lexicographic scanning, time-based versioning, TTL semantics, and efficient mutation operations.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerImplement an in-memory key-field-value DB with TTL
This question evaluates implementation skills in efficient in-memory data structures, time-to-live (TTL) expiration semantics, ordered and prefix scans, and snapshot backup/restore mechanisms.
Coding & AlgorithmsOnline Assessment - MetaEasySoftware EngineerImplement array parity and tree level views
This problem tests your ability to handle two distinct sub-tasks: identifying array elements with unique parity and performing a level-order traversal of a binary tree.
Coding & AlgorithmsTechnical ScreenArraysHash MapsBinary Trees - 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 cd and merge intervals
This problem tests your ability to manipulate string-based file system paths using a stack-based approach and consolidate overlapping time intervals using sorting.
Coding & AlgorithmsTechnical ScreenStringsStackSorting - MetaMediumMachine Learning EngineerImplement exponentiation and fill grid distances
This problem tests proficiency in efficient modular exponentiation (binary exponentiation) and multi-source Breadth-First Search (BFS) for computing shortest distances in a grid.
Coding & AlgorithmsOnline AssessmentBFSModular ArithmeticDivide and Conquer - MetaMediumMachine Learning EngineerImplement exponentiation and link tree neighbors
This question evaluates numerical algorithm implementation and in-place tree traversal skills, specifically fast exponentiation with careful edge-case and precision handling and iterative linking of next pointers in a perfect binary tree.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerImplement fast power and k-palindrome
This problem tests your ability to implement binary exponentiation for efficient numerical calculation and dynamic programming for optimal string manipulation.
Coding & AlgorithmsTechnical ScreenRecursionDynamic ProgrammingMath - MetaEasySoftware EngineerImplement four coding challenges
This set of four problems evaluates algorithmic problem-solving and implementation skills across deterministic simulation (grid-based placement), dynamic interval/segment maintenance, incremental numeric tracking, and greedy array transformation, emphasizing correctness, state management, and complexity reasoning.
Coding & AlgorithmsOnline Assessment - MetaHardSoftware EngineerImplement held transfers with accept/cancel
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 Implement held transfers with accept/cancel states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnline Assessment - MetaMediumSoftware EngineerImplement in-place duplicate removal
This question evaluates proficiency in in-place array manipulation, handling duplicates in sorted sequences, and formal reasoning about space and time complexity.
Coding & AlgorithmsTechnical Screen - MetaEasySoftware EngineerImplement list cloning and k-frequency finder
The first part evaluates understanding of linked data structures and deep-copy semantics, focusing on preserving node values and both next and arbitrary random pointer relationships while reasoning about node identity and cycles, and is commonly asked to assess correctness in memory/reference handling and structural preservation.
Coding & AlgorithmsTechnical Screen
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