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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 3691–3720 of 4616 results
- SnapchatMediumData ScientistCalculate Average Camera Switches per Session
Tests the ability to write a PostgreSQL query using window functions (LAG) to compute the average number of specific state transitions per session while accounting for zero-transition sessions and avoiding integer division.
Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLWindow Functions - SnapchatMediumSoftware EngineerCheck if a binary tree is complete
This problem tests your understanding of binary tree properties and your ability to perform a level-order traversal to verify structural completeness.
Coding & AlgorithmsTechnical ScreenBinary TreeBFSData Structures - SnapchatMediumMachine Learning EngineerCompare convolutions and transformers
This question evaluates understanding of neural network architectures, focusing on inductive biases, data and compute trade-offs, mechanisms for handling long-range dependencies, domain-specific roles in vision and NLP, and modern hybrid approaches that address architecture limitations.
Machine LearningTechnical Screen - SnapchatMediumData ScientistCompute same-day acceptance metrics last week
This question evaluates proficiency in time-based data manipulation, SQL JOINs and aggregations, handling NULLs and missing reference rows, and computing acceptance and percentage metrics with careful UTC date boundary treatment.
Data Manipulation (SQL/Python)Technical Screen - SnapchatMediumSoftware EngineerCompute sliding window median
This question evaluates the ability to maintain a running median over a sliding window, testing competencies in data structures, order statistics, dynamic updates, and algorithmic efficiency within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - SnapchatMediumData ScientistCount Users Whose First Daily Event Is App Launch
Write a PostgreSQL query to identify each user's first event per calendar day and aggregate the distinct user count by operating system and device when that first event is an app launch. Tests window functions, tie-breaking logic, and multi-dimensional grouping.
Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLWindow Functions - SnapchatMediumSoftware EngineerCount islands with eight-direction adjacency
This question evaluates a candidate's competency in graph traversal algorithms and data-structure trade-offs by counting connected components on a grid with eight-direction adjacency, focusing on BFS/DFS behavior and optional Union-Find approaches.
Coding & AlgorithmsTechnical Screen - SnapchatMediumMachine Learning EngineerDescribe a challenging project you led
This question evaluates leadership, project management, stakeholder communication, and technical problem-solving skills in a machine learning engineering context, with emphasis on trade-off analysis and impact measurement.
Behavioral & LeadershipTechnical Screen - SnapchatHardMachine Learning EngineerDesign a ranking system pipeline
This question evaluates competency in end-to-end ML system design for ranking and recommendation, covering problem definition, data and feature pipelines, offline training, online inference, model refresh, monitoring, and handling cold-start, exploration, feedback loops, and model drift.
ML System DesignTechnical Screen - SnapchatMediumMachine Learning EngineerDesign a short-video recommender for short-term interest
This problem tests your ability to design a real-time recommendation system that captures dynamic user intent over short sessions, balancing latency, feature freshness, and algorithmic relevance.
ML System DesignTechnical ScreenRecommender SystemsReal-time InferenceSession-based Models - SnapchatHardData ScientistDesign an experiment for spam filtering impact
This question evaluates experimental design and causal inference skills for measuring the effect of a stricter spam filter on same-day friend-request acceptance, covering hypothesis specification, interference and randomization choices, metric definition and windows, power calculation, and handling incomplete labels.
Analytics & ExperimentationTechnical Screen - SnapchatMediumSoftware EngineerDesign and Implement a Thread-Safe LRU Cache
Design an `O(1)` LRU cache and extend it with clear thread-safety semantics. The solution covers hash-map and doubly linked-list invariants, boundary cases, linearization points, single-lock correctness, sharding trade-offs, eviction callbacks, and concurrent testing.
Software Engineering FundamentalsTechnical Screen - SnapchatHardSoftware EngineerDesign short URL service with click counting
This question tests the ability to design a distributed URL shortening service, focusing on system architecture, database partitioning, and strategies for high-frequency write operations like click tracking.
System DesignTechnical ScreenScalabilityDatabaseCaching - SnapchatMediumMachine Learning EngineerDesign short-video retrieval with sparse text
This question evaluates expertise in ML system design, large-scale information retrieval and recommendation engineering, with emphasis on multi-modal video representation, sparse-text handling, indexing/ANN choices, low-latency online serving, and bias mitigation such as popularity bias.
ML System DesignTechnical Screen - SnapchatMediumMachine Learning EngineerDetermine Whether Courses Can Be Completed
This question evaluates understanding of graph modeling, directed cycle detection, and dependency resolution in the context of course prerequisites. It is commonly asked in the coding & algorithms domain because it reveals a candidate's algorithmic problem-solving ability and practical application of graph algorithms (expected O(V+E) time), testing practical implementation skills rather than only conceptual theory.
Coding & AlgorithmsTechnical Screen - SnapchatMediumData ScientistDiagnose Violations of Linear Regression Assumptions
Tests a candidate's systematic understanding of ordinary least squares (OLS) regression assumptions, distinguishing how specific violations compromise unbiased point estimation, standard error inference, and predictive accuracy.
Statistics & MathTechnical ScreenStatisticsLinear RegressionEconometrics - SnapchatMediumMachine Learning EngineerEvaluate an Integer Arithmetic Expression String With Operator Precedence
A coding problem to evaluate a string expression of non-negative integers with addition, subtraction, multiplication and integer division, respecting operator precedence and left-to-right order, with no parentheses and no eval. It tests parsing, precedence handling and truncating integer division.
Coding & AlgorithmsTechnical Screen - SnapchatMediumMachine Learning EngineerExplain CLIP, contrastive losses, and retrieval limits
This question evaluates understanding of multi-modal representation learning and retrieval systems, covering CLIP-style joint image–text encoders, contrastive loss families, embedding-based retrieval limitations, alternative retrieval paradigms, and issues like popularity bias.
Machine LearningTechnical Screen - SnapchatHardMachine Learning EngineerExplain Core ML Training, Evaluation, and Transformer Concepts
Tests end-to-end understanding of machine learning fundamentals, covering regularization against overfitting, handling severe class imbalance, optimizer mechanics, threshold calibration, and Transformer self-attention dynamics.
Machine LearningTechnical Screenmachine-learningdeep-learningtransformers - SnapchatHardMachine Learning EngineerExplain Embeddings, Positive Labels, and Retraining in a Recommendation Project
Discuss recommendation-project ownership, embeddings, positive labels and retraining with attention to exposure bias and data leakage.
Machine LearningTechnical Screen - SnapchatHardMachine Learning EngineerExplain LLM tuning and transformer basics
This question evaluates competencies in large language model tuning and Transformer fundamentals, covering fine-tuning strategies, dataset construction and labeling, model adaptation choices, loss functions and evaluation metrics, regularization techniques, optimizer selection, self-attention and multi-head attention, and the end-to-end Transformer decoder mathematical steps. It is commonly asked in Machine Learning interviews because it probes both conceptual understanding and practical application—examining architectural trade-offs, optimization and regularization decisions, deployment constraints, and reasoning about failure modes such as overfitting and hallucination within the Machine Learning domain.
Machine LearningTechnical Screen - SnapchatMediumMachine Learning EngineerExplain Overfitting and Transformer Attention
This question evaluates understanding of model generalization and regularization techniques alongside Transformer self-attention and positional encoding, assessing competencies in diagnosing overfitting, applying appropriate mitigation strategies, and interpreting attention mechanisms.
Machine LearningTechnical Screen - SnapchatHardMachine Learning EngineerExplain Vanishing Gradients in Neural-Network Training
Diagnose vanishing gradients through the chain rule, activation and gradient checks, and architecture-appropriate training remedies.
Machine LearningTechnical Screen - SnapchatMediumSoftware EngineerExplain median vs mean for L1/L2
This question evaluates understanding of central tendency and loss functions—specifically the roles of median and mean under L1 (absolute) and L2 (squared) norms—and the geometric extension of these concepts to two-dimensional data.
Statistics & MathTechnical Screen - SnapchatMediumSoftware EngineerFind Duplicate Files with a Hand-Written Directory Traversal
This problem tests iterative directory traversal techniques, efficient file hashing strategies to handle large data, and the ability to manage edge cases like file system inconsistencies or symbolic links.
Software Engineering FundamentalsTechnical ScreenFile SystemHashingIteration - SnapchatMediumSoftware EngineerFind Maximum Island Perimeter
This problem tests grid traversal techniques (DFS/BFS) and the ability to calculate boundary contributions within a connected component in a 2D matrix.
Coding & AlgorithmsTechnical ScreenDFSBFSGrid - SnapchatMediumSoftware EngineerFind index of first unique character
Find index of first unique character 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 - SnapchatMediumSoftware EngineerFind max node-value range across components
This problem tests your ability to traverse an undirected graph to identify connected components and perform aggregations over the nodes within each component.
Coding & AlgorithmsTechnical ScreenGraphDFSBFS - SnapchatHardMachine Learning EngineerFind the Smallest Range Covering K Sorted Lists
Tests multi-pointer tracking and priority queue fundamentals to find the narrowest bounding interval containing at least one element from each of k sorted lists.
Coding & AlgorithmsTechnical ScreenHeapSliding WindowGreedy - SnapchatMediumSoftware EngineerHandle cards, islands, Trie, median
These problems evaluate proficiency in core data structures and algorithmic problem-solving, covering grouping/counting techniques, grid-based island detection, trie (prefix tree) design, and streaming median computation within the domain of data structures and algorithms.
Coding & AlgorithmsTechnical Screen
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