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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 8371–8400 of 9403 results
- TikTokHardMachine Learning EngineerDefine QKV for recommender cross-attention
This question evaluates understanding of Transformer-style cross-attention and the concrete design of Query, Key, and Value tensors for deep-learning recommender systems, testing representation semantics, embedding alignment, and interaction modeling between user history, candidate items, and context.
Machine LearningTechnical Screen - TikTokMediumSoftware EngineerDelete nodes in linked list and binary tree
This question evaluates proficiency in mutable data structure operations, specifically linked list node removal and binary tree node replacement/deletion, requiring knowledge of traversal, pointer/reference manipulation, and edge-case handling.
Coding & AlgorithmsTechnical Screen - TikTokMediumData ScientistDemonstrate leadership in cross-functional disagreement
This question evaluates leadership, cross-functional collaboration, stakeholder management, experimental design, and metric-driven decision-making competencies in a data scientist context, with emphasis on trade-off reasoning and end-to-end execution.
Behavioral & LeadershipHR Screen - TikTokMediumData ScientistDerive L1 vs L2 effects with correlation
This question evaluates understanding of regularization methods (ridge/L2, LASSO/L1, and elastic net), multicollinearity effects, geometric intuition of constraint sets, and hyperparameter influence on coefficient patterns in the Statistics & Math domain.
Statistics & MathTechnical Screen - TikTokMediumData ScientistDescribe Your Most Impactful Project Experience and Lessons Learned
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Your Most Impactful Project Experience and Lessons Learned states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - TikTokMediumData EngineerDescribe a data-heavy project
Describe a data-heavy project 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 & LeadershipTechnical Screen - TikTokMediumSoftware EngineerDescribe a project you are proud of
This question evaluates technical leadership, ownership, system design, execution, collaboration, trade-off reasoning, and the ability to demonstrate measurable impact from a software project.
Behavioral & LeadershipTechnical Screen - TikTokMediumSoftware EngineerDescribe a service performance challenge
This question evaluates competency in production performance engineering, observability-driven diagnosis, experimentation and impact quantification, as well as leadership in prioritizing trade-offs and risk mitigation.
Behavioral & LeadershipTechnical Screen - TikTokMediumSoftware EngineerDescribe career plan and teamwork approach
This question evaluates a candidate's career planning, self-awareness, and teamwork competencies, specifically goal setting, alignment with team priorities, conflict resolution, and collaborative decision-making.
Behavioral & LeadershipTechnical Screen - TikTokMediumMachine Learning EngineerDescribe internship and research projects
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe internship and research projects states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - TikTokMediumSoftware EngineerDescribe toughest challenge and resolution
This question evaluates a candidate's problem-solving, leadership, communication, decision-making, and ability to quantify impact when resolving complex technical or organizational challenges.
Behavioral & LeadershipTechnical Screen - TikTokHardData ScientistDesign A/B Test for Cost-Per-Conversion Efficiency Analysis
Design A/B Test for Cost-Per-Conversion Efficiency Analysis evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - TikTokMediumSoftware EngineerDesign LRU cache with O(1) operations
This question evaluates a candidate's understanding of data structures and algorithm design, specifically skills in implementing constant-time cache operations, eviction policies, update handling, and concurrency/thread-safety considerations.
Coding & AlgorithmsTechnical Screen - TikTokMediumData ScientistDesign Metrics for Content Moderation and Chatbot Evaluation
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design Metrics for Content Moderation and Chatbot Evaluation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - TikTokHardData ScientistDesign Real-Time Credit Card Fraud Detection System
Evaluates real-time credit-card fraud detection system design under latency, drift, and asymmetric costs. Strong answers cover data, delayed labels, features, models, serving architecture, monitoring, retraining, and thresholding.
Machine LearningOnsite - TikTokHardData EngineerDesign a Warehouse for Key Metrics
This question evaluates a data engineer's skills in dimensional modeling, data warehousing, and analytics infrastructure, covering facts and dimensions, grain definition, surrogate keys, partitioning and clustering strategies, and operational concerns like deduplication, late-arriving events, null/anonymous users, and slowly changing dimensions.
System DesignTechnical Screen - TikTokMediumData ScientistDesign a creator posting-frequency experiment
This problem tests the ability to design a controlled experiment while accounting for network interference, non-compliance, and selection bias in a two-sided platform.
Analytics & ExperimentationOnsiteExperimentationA/B TestingCausal Inference - TikTokMediumSoftware EngineerDesign a distributed key-value store
This problem tests your ability to design a scalable, fault-tolerant distributed key-value store, focusing on partitioning strategies, replication, and handling node failures.
System DesignTechnical ScreenDistributed SystemsScalabilityHashing - TikTokHardSoftware EngineerDesign a high-concurrency ticketing system
TikTok software-engineer system-design interview question: design a high-concurrency ticketing / flash-sale system that survives millions of concurrent purchase attempts with zero oversell. It tests admission control and fairness (virtual waiting room, FIFO vs lottery), atomic inventory allocation with TTL reservations, anti-bot defenses, idempotent order creation, hot-key sharding, the data model and APIs, caching, message queues and delivery semantics, consistency and reconciliation, failure handling, capacity planning, SLOs, load testing, and monitoring.
System DesignTechnical Screen - TikTokMediumMachine Learning EngineerDesign a model to choose dynamic K
This question evaluates a candidate's competency in ML system design and decision-focused modeling for recommender pipelines, focusing on retrieval-ranking trade-offs, latency and compute cost constraints, and dynamic per-request selection of K.
ML System DesignOnsite - TikTokHardData ScientistDesign an ad-selection system across objectives
This question evaluates expertise in machine learning system design for ad selection and real-time bidding, covering predictive modeling and calibration, value unification across objectives, auction mechanics, budget pacing, exploration strategies, guardrails, and evaluation methodologies.
Machine LearningTechnical Screen - TikTokHardData ScientistDesign an experiment for exploratory recommendations
Evaluates experiment design for exploratory recommendation algorithms that may trade short-term engagement for discovery. Strong answers define exploration, satisfaction, retention, ecosystem health, leading indicators, and cautious launch decisions.
Analytics & ExperimentationTechnical Screen - TikTokHardData ScientistDesign an interference-robust A/B test for monetization
This question evaluates experimental design and causal inference skills within Analytics & Experimentation, emphasizing interference mitigation, clustering and randomization choices, eligibility and exposure rules, metric hierarchy and guardrails, power and duration planning, variance-reduction techniques, and cluster-robust inference in a two-sided marketplace. It is commonly asked to test a data scientist's practical-application ability to balance monetization and growth trade-offs through precise statistical planning, pre-registered stopping rules, SRM detection, MDE and rollout decision thresholds rather than purely conceptual understanding.
Analytics & ExperimentationHR Screen - TikTokHardSoftware EngineerDesign and explain your project architecture
A TikTok software-engineer system-design screen that asks you to walk through one of your recent projects end-to-end: architecture diagram, components and data stores, sync vs. async data flows and protocols, scaling and capacity planning, fault tolerance, backpressure, idempotency, observability, and security. It also probes the design trade-offs you made, the real failure modes you hit, the measurable impact, and what you would redesign. Includes a fully worked reference answer plus the scoring rubric.
System DesignTechnical Screen - TikTokEasySoftware EngineerDesign automated regression tests for an API
This question assesses the ability to structure a comprehensive automated regression suite for a RESTful API, focusing on test case selection, validation strategies, and maintenance.
Software Engineering FundamentalsTechnical ScreenAPIQACI/CD - TikTokMediumSoftware EngineerDesign low-latency large-scale hotel booking system
This question evaluates understanding of large-scale backend and distributed system design, including scalability, high availability, consistency models, concurrency control to prevent double-booking, hotspot mitigation, and protocol trade-offs for low-latency booking and availability flows.
System DesignTechnical Screen - TikTokEasyData ScientistDesign multimodal deployment under compute limits
This question evaluates competency in Machine Learning systems engineering for a Data Scientist role, covering multimodal model deployment and inference optimization, scalable retrieval (ANN and hybrid search), generalization and regularization concepts (overfitting, dropout), normalization methods, and RLHF, emphasizing both theoretical principles and engineering feasibility. It is commonly asked to assess reasoning about trade-offs between quality, latency, and cost in resource-constrained environments, validation via offline/online metrics, and the ability to bridge conceptual understanding with practical deployment and system-level design considerations.
Machine LearningOnsite - TikTokHardSoftware EngineerDesign overload protection with load shedding
Design overload protection with load shedding evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - TikTokHardMachine Learning EngineerDesign query generation to maximize CTR
Design query generation to maximize CTR evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - TikTokHardData ScientistDesign recommendations objective balancing growth and monetization
This question evaluates a data scientist's competency in designing multi-objective recommender systems that balance user growth and creator monetization, covering ranking objective formulation, per-impression value estimation, constraint enforcement (latency, diversity, fairness/exposure), offline-to-online metric validation, cold-start and exploration strategies, and interference/attribution challenges within the Machine Learning domain. It is commonly asked to assess the ability to reason about trade-offs between business and user metrics, to define measurable objectives and validation plans, and to demonstrate both conceptual understanding and practical application of system design and evaluation.
Machine LearningHR Screen
TikTok