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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 4051–4080 of 4616 results
- 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 - 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 - 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 - 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 - 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 robust A/B test with interference and seasonality
This question evaluates expertise in experimental design, causal inference, statistical power and minimum detectable effect calculation, variance-reduction techniques, sequential monitoring, and diagnostics for interference, spillovers, and weekly seasonality in A/B testing.
Analytics & ExperimentationTechnical Screen - TikTokMediumSoftware EngineerDesign system to detect privacy-leak records
This question evaluates a candidate's ability to design scalable ML-driven systems for detecting and classifying privacy-sensitive and PII-containing records across structured and unstructured data, testing competencies in data engineering, machine learning (including deep learning and LLM/RAG integrations), system architecture, and privacy/security considerations. It is commonly asked to probe reasoning about functional and non-functional requirements, trade-offs in detection and classification approaches, evaluation metrics like precision and recall, feedback loops and operational scaling, and it falls under ML system design with a practical, application-level focus rather than purely conceptual abstraction.
ML System DesignTechnical Screen - TikTokHardSoftware EngineerDesign tables from metrics
This question evaluates analytics data modeling and event-driven warehousing skills, including schema design, metric derivation, partitioning/clustering strategies, and data-quality concerns such as deduplication, late arrivals, and idempotency.
System DesignTechnical Screen - TikTokMediumMachine Learning EngineerDesign video captioning under compute limits
This question evaluates expertise in ML system design for multimodal large models, covering deployment under compute and GPU memory constraints as well as large-scale retrieval and processing of video captions and embeddings.
ML System DesignTechnical Screen - TikTokMediumMachine Learning EngineerDetermine if a string can be segmented
This question evaluates a candidate's understanding of dynamic programming and string-processing skills, along with algorithmic complexity analysis for segmentation problems.
Coding & AlgorithmsTechnical Screen - TikTokMediumData ScientistDiagnose Traffic Allocation in A/B Test Results
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 Diagnose Traffic Allocation in A/B Test Results states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - TikTokMediumMachine Learning EngineerDifferentiate virtual and pure virtual functions
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Differentiate virtual and pure virtual functions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - TikTokMediumSoftware EngineerDiscuss Python mutability, copying, and GIL
This question evaluates understanding of Python mutability and copying semantics — including how mutable versus immutable objects affect function arguments and container behavior — and knowledge of the Global Interpreter Lock (GIL) and concurrency models.
Data Manipulation (SQL/Python)Technical Screen - TikTokHardData ScientistEstimate heterogeneous treatment effects with causal ML
This question evaluates a candidate's competency in causal machine learning, heterogeneous treatment effect estimation, off-policy evaluation, and fairness-aware policy design using observational data.
Machine LearningTechnical Screen - TikTokMediumSoftware EngineerExplain C++ containers, segfaults, and virtual dispatch
This question assesses the candidate's deep understanding of C++ memory management, the runtime implementation of polymorphism, and the performance characteristics of standard containers.
Software Engineering FundamentalsTechnical ScreenC++Memory ManagementPolymorphism - TikTokMediumSoftware EngineerExplain DPO and construct its training data
This question evaluates understanding of Direct Preference Optimization (DPO) for fine-tuning large language models, assessing conceptual differences from PPO-based RLHF and the competency to design pairwise preference training datasets.
Machine LearningTechnical Screen - TikTokMediumMachine Learning EngineerExplain FlashAttention, KV cache, and RoPE
This problem tests your understanding of core transformer efficiency techniques, specifically focusing on how they improve memory footprint, latency, and sequence length handling.
Machine LearningTechnical ScreenTransformersAttentionInference Optimization - TikTokMediumSoftware EngineerExplain Java ConcurrentHashMap and queues
This question evaluates a candidate's understanding of Java concurrency primitives and concurrent collection implementations, focusing on thread-safety, synchronization mechanisms, lock-free techniques, and blocking versus non-blocking queue behavior.
Software Engineering FundamentalsTechnical Screen - TikTokMediumSoftware EngineerExplain Java DI, AOP, and transactions
This question evaluates understanding of Java framework internals and related competencies including dependency injection, aspect-oriented programming and proxy mechanisms, transaction management, annotation processing, bean lifecycle, classpath scanning, bean scopes, and circular dependency detection or resolution.
Coding & AlgorithmsTechnical Screen - TikTokHardSoftware EngineerExplain SRE architecture and troubleshooting scenarios
This question evaluates a candidate's competency in SRE architecture and reliability engineering, covering Kubernetes fundamentals, pod failure troubleshooting, SLO/SLA/SLI reasoning, multi-layer latency diagnosis, and SQL versus NoSQL trade-offs within the System Design domain.
System DesignTechnical Screen - TikTokEasySoftware EngineerExplain TLS Handshakes and HTTPS Traffic Inspection
Tests understanding of the TLS handshake, asymmetric vs. symmetric cryptography in HTTPS, certificate authority verification, and the mechanics of forward secrecy versus intercepting HTTPS proxies.
Software Engineering FundamentalsTechnical ScreenNetworkingSecurityTLS/SSL - TikTokMediumMachine Learning EngineerExplain Transformer, GPT vs BERT, and PR metrics
This question evaluates understanding of modern NLP model architecture and evaluation metrics, specifically Transformer block components, key distinctions between GPT and BERT including pretraining objectives and usage, and precision/recall interpretation, testing competencies in deep learning architecture knowledge, model selection reasoning, and metric-based evaluation within the Software Engineering Fundamentals domain for a Machine Learning Engineer role. It is commonly asked because it probes both conceptual understanding of architectures and pretraining paradigms and practical application of performance trade-offs—assessing conceptual understanding alongside practical application in threshold-based precision versus recall decisions.
Software Engineering FundamentalsTechnical Screen - TikTokMediumData EngineerExplain Your Resume and Behavioral Examples
This question evaluates behavioral and leadership competencies within data engineering, focusing on impact measurement, decision-making under ambiguity, incident response and mitigation, stakeholder management, prioritization under schedule changes, and mission alignment for public-sector contexts.
Behavioral & LeadershipTechnical Screen - TikTokMediumData EngineerExplain a SQL query result
This question evaluates proficiency in SQL result-set interpretation and reasoning about joins, filters, aggregations, window functions, NULL handling, duplicate rows, and ordering within the Data Manipulation (SQL/Python) domain for a Data Engineer role.
Data Manipulation (SQL/Python)Technical Screen - TikTokMediumSoftware EngineerExplain a challenging project end-to-end
This question evaluates project ownership, cross-functional leadership, technical decision-making, and metrics-driven execution within a software engineering Behavioral & Leadership interview, focusing on competencies such as stakeholder coordination, data-informed trade-offs, and operational reliability.
Behavioral & LeadershipTechnical Screen
TikTok