practice a little every day, paws crossed ♡
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 2461–2490 of 4616 results
- MetaEasyData ScientistHow to evaluate a similar-listing notifications feature
A Meta Data Scientist analytics-and-experimentation interview question: a US C2C second-hand marketplace is considering opt-in “similar listings” notifications. You must (1) decide whether to build it — hypotheses, opportunity sizing, risks, MVP plan — and (2) design the post-launch measurement: a user-randomized A/B test with a primary/diagnostic/guardrail metric stack, handling of opt-in selection bias, and a clear launch/rollback rule.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistHow to evaluate new listing notifications?
This problem tests your ability to design an A/B test for a marketplace notification feature, focusing on balancing buyer engagement with potential cannibalization and ecosystem health metrics.
Analytics & ExperimentationTechnical ScreenA/B TestingMetricsMarketplace - MetaEasyData ScientistHow would you define and use retention metrics?
This question evaluates a data scientist's competency in product analytics and experimentation by assessing understanding of retention metric definitions, cohorting strategies, short- versus long-term trade-offs, common measurement pitfalls, and A/B test design considerations.
Analytics & ExperimentationTechnical Screen - MetaEasyData ScientistHow would you design a Shop Ads ranking algorithm?
This problem tests the ability to design an end-to-end ad ranking system, focusing on objective optimization, feature engineering, and mitigating common biases in marketplace environments.
Machine LearningTechnical ScreenSystem DesignRankingAds - MetaEasyData ScientistHow would you evaluate pixel-issue notifications?
This question tests the ability to build a comprehensive evaluation framework for a new feature, specifically focusing on defining success metrics, identifying potential side effects, and designing an experimental setup for pixel-issue notifications.
Analytics & ExperimentationTechnical ScreenMetric DesignA/B TestingCausal Inference - MetaEasyData ScientistHow would you evaluate upranking shop ads?
This question tests your ability to design an A/B testing framework for evaluating ad ranking changes, including metric selection, segment analysis, and identifying potential biases in historical data.
Analytics & ExperimentationTechnical ScreenA/B TestingMetricsAdTech - MetaMediumData ScientistHow would you measure Group Call success?
This question evaluates a data scientist's ability to design a measurement framework encompassing product goal definition, north-star metric selection, success metrics for adoption, engagement and quality, precise retention definitions (app-level vs feature-specific and creator vs participant), cohort retention windows, and experimental design including guardrails, bias sources and network effects. It is asked in the Analytics & Experimentation domain to assess practical application of product analytics and quantitative reasoning about short-term versus long-term tradeoffs, segmentation and experimental validity, operating at a primarily practical application level with necessary conceptual understanding.
Analytics & ExperimentationTechnical Screen - 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 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 - 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 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 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 - 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 - 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 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 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 - 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 - MetaMediumSoftware EngineerImplement paginated, sortable dynamic table component
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 paginated, sortable dynamic table component states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Software Engineering FundamentalsTechnical Screen - MetaMediumSoftware EngineerImplement right side view and local minimum search
This question evaluates a candidate's understanding of binary tree traversal and array local-minimum detection, measuring skills in data structures (binary trees and arrays), algorithm design, and algorithmic time-complexity analysis.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerImplement string dedup and mirror tree traversal
This question evaluates string-processing and tree-traversal algorithm skills, focusing on efficient group-deletion and streaming-capable string dedup operations and a mirrored boundary traversal of binary trees, and it tests algorithmic design, complexity guarantees, and correctness/termination reasoning.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerImplement string sum and weighted city picker
Implement string sum and weighted city 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 & AlgorithmsTechnical Screen - MetaMediumMachine Learning EngineerImplement the Forward Pass of Multi-Head Attention From Scratch
An ML coding question that asks for the forward pass of multi-head attention from scratch. It tests query, key and value projections, splitting and merging heads, scaled dot-product attention with a stable softmax, causal and padding masks, complexity in sequence length, and self-made correctness checks.
Machine LearningTechnical Screen - MetaHardSoftware EngineerImplement, Debug, and Optimize a React Table
Implement, Debug, and Optimize a React Table evaluates requirements, assumptions, structured reasoning, trade-offs, and verification in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Software Engineering FundamentalsTechnical Screen - MetaEasyData ScientistInvestigate why an advertiser’s spend decreased
This question tests your ability to systematically isolate the root cause of a drop in advertising spend by decomposing metrics and evaluating external versus internal factors.
Analytics & ExperimentationTechnical ScreenRoot Cause AnalysisMetrics DecompositionAdTech - MetaMediumSoftware EngineerMap Random Tickets to Population-Weighted Cities
Map deterministic tickets to cities in proportion to their population weights by building cumulative ranges and locating each possibly repeated ticket efficiently.
Coding & AlgorithmsTechnical Screen - MetaMediumMachine Learning EngineerMasked Multi-Head Attention Forward Pass Without a Framework
Implement batched multi-head self-attention by hand, including projections, head split and merge, scaled dot products, a numerically stable masked softmax, and causal and padding masks, then prove it correct with tests you design yourself. Tests shape discipline and attention fundamentals.
Machine LearningTechnical Screentransformersattentionnumerical-stability
Meta