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 8401–8430 of 9403 results
- 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 - TikTokHardProduct ManagerDetecting and Mitigating Cheating in Games
Practice designing an anti-cheat product strategy for competitive online games with real-time matches, ranked leaderboards, and an in-game economy. The solution covers detection signals, streaming and batch pipelines, modeling approaches, prevention, graduated enforcement, appeals, false-positive recovery, and trust metrics.
Product Design & StrategyOnsite - TikTokMediumMachine Learning EngineerDetermine frog reachability across stones
This question evaluates dynamic programming, state-space reachability, and constrained transition reasoning, along with skills in hash-based memoization and time/space complexity analysis.
Coding & AlgorithmsOnsite - 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 Search Issues with Relevant Metrics and Solutions
Evaluates diagnosis of brand-name search relevance and safety issues using analytics and experimentation. Strong answers define relevance, satisfaction, and harmful-content metrics, find root causes, and validate fixes offline and online.
Analytics & ExperimentationOnsite - 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 - TikTokHardData ScientistDiagnose metric drop in Ads Manager
This question evaluates a Data Scientist's competency in operational analytics, metric integrity, root-cause analysis, and experiment interpretation, covering concepts such as logging latency, sample ratio assessment, and product instrumentation awareness.
Analytics & ExperimentationOnsite - 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 - TikTokMediumProduct ManagerDigital Marketing Campaign ROI Analysis
Practice allocating budget between two TikTok mobile-game ad creatives using funnel and ROI analysis. The solution covers CTR, CVR, IPM, CPI, ARPU, ROAS, marginal return, creative fatigue, growth headroom, long-term LTV, confidence, and exploration-versus-exploitation decisions.
Product / Decision MakingOnsite - 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 ScientistExplain Your Experience and Interest in Tech Role
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Explain Your Experience and Interest in Tech Role states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipHR 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 - TikTokHardSoftware EngineerExplain Your System Architecture
Explain Your System Architecture 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 DesignOnsite - TikTokMediumBackend Software EngineerExplain a Difficult Project and Its Scaling Decisions
Practice explaining a difficult engineering project through ownership, technology choices, scaling constraints, and measured trade-offs. The model answer uses a clearly hypothetical ingestion service to show how to discuss alternatives, bottlenecks, rollout evidence, and lessons without exposing private project details.
Behavioral & LeadershipOnsite - 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 - TikTokHardData ScientistExplain and tune XGBoost; prevent overfitting
This question evaluates understanding of XGBoost tree-boosting internals, hyperparameter impacts on bias/variance/training time, strategies for imbalanced binary classification and leakage-aware validation, post-training calibration, and interpretability for tabular fraud detection.
Machine LearningTechnical Screen - TikTokMediumSoftware EngineerExplain gap and project contributions
This question evaluates a candidate's ability to explain employment gaps and provide a project deep-dive demonstrating ownership, technical decision-making, measurable impact, and clear delineation of personal contributions, testing communication, accountability, and system design competencies.
Behavioral & LeadershipTechnical Screen
TikTok