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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 1891–1920 of 9403 results
- ByteDanceMediumSite Reliability EngineerSolve Stack and String Shift Problems
This problem tests your ability to handle nested validation using stacks and coordinate transformation using modular arithmetic on strings.
Coding & AlgorithmsTechnical ScreenStackStringsModulo Arithmetic - ByteDanceMediumSoftware EngineerSolve interval merging and histogram area
This question evaluates algorithmic problem-solving skills focused on interval manipulation and histogram-based area computation, emphasizing data structures, ordering, and reasoning about time and space complexity.
Coding & AlgorithmsTechnical Screen - ByteDanceMediumSoftware EngineerSort Every Concentric Matrix Border Clockwise
Sort the values on every concentric border of a rectangular matrix and write them back clockwise from each border's top-left cell. Traverse corners exactly once, preserve interior layer boundaries, handle single-row or single-column centers, and leave the input unchanged.
Coding & AlgorithmsOnline Assessment - ByteDanceHardSoftware EngineerSplit n People Into Two Groups So No Pair Who Dislike Each Other Shares a Group
Given n people and a list of pairs who dislike each other, decide whether everyone can be divided into two groups of any size so that no disliking pair shares a group. The problem tests reasoning about pairwise constraints, people with no constraints, and a solution that runs in time linear in the number of people and pairs.
Coding & AlgorithmsTechnical Screen - ByteDanceMediumSoftware EngineerTCP vs UDP: Key Differences and the Applications That Suit Each
From a TikTok backend intern interview: compare TCP and UDP, including connection setup, reliability, ordering, flow and congestion control, message boundaries and overhead, and explain which applications suit each protocol. It tests tying transport-layer guarantees and their costs to real application needs.
Software Engineering FundamentalsTechnical Screen - ByteDanceHardSoftware EngineerTell Me About a Challenge You Faced at Work and How You Worked Through It
A behavioral question from a one-hour software engineering phone screen asks you to describe a challenge you faced and how you handled it. It tests whether you can pick a story with real stakes, make your own decisions and reasoning clear, report a concrete result, and reflect on what you would change, all within a few minutes.
Behavioral & LeadershipTechnical Screen - ByteDanceMediumProduct ManagerTell me about your fit
Prepare a TikTok PM career-story answer covering self-introduction, role fit, Company A ownership, project deep dive, job changes, moving to the United States, long-term PM goals, relocation flexibility, metrics, and ecosystem product judgment.
Behavioral & LeadershipTechnical Screen - ByteDanceMediumSoftware EngineerTrace and Debug Agent Tool-Call Failures
Design a distributed observability and tracing system to monitor, debug, and diagnose multi-step agent tool-call failures across retries and execution stages without leaking sensitive data.
System DesignTechnical ScreenDistributed TracingObservabilityAI Agents - ByteDanceEasySite Reliability EngineerTriage a Suddenly Slow Production Service
This question tests your structured approach to incident response and root cause analysis in a distributed production environment.
Software Engineering FundamentalsTechnical ScreenSREIncident ResponseDistributed Systems - ByteDanceEasySite Reliability EngineerValidate Matching Brackets
Tests stack-based parsing fundamentals, early mismatch detection, and linear-time string validation for balanced bracket sequences.
Coding & AlgorithmsTechnical Screenstackstringsparsing - ByteDanceMediumApplied ScientistWrite the Cross-Entropy Loss and Compute Precision, Recall and F1
An ML fundamentals question in two parts: write the cross-entropy loss for binary and multi-class classification and explain why it is used, then compute precision, recall, F1 and accuracy for a described imbalanced classifier. It tests exact formulas, careful arithmetic and metric choice.
Machine LearningTechnical Screen - C3 AIMediumData ScientistChoose Tokenization and Dense or Sparse Retrieval
This problem tests your understanding of document retrieval strategies, specifically comparing sparse and dense representations and how to integrate them into a production RAG system.
Machine LearningTechnical ScreenRAGNLPInformation Retrieval - C3 AIMediumData ScientistChoose a Parameter to Reduce Underfitting in Gradient-Boosted Trees
Explain why decreasing minimum observations per leaf can reduce gradient-boosted tree underfitting and how to validate the change.
Machine LearningOnline Assessment - C3 AIMediumData ScientistCompare How Models Respond to Multicollinearity
Compare multicollinearity effects in regression, Lasso, logistic regression, decision trees, and boosted trees without overstating immunity.
Machine LearningOnline Assessment - C3 AIHardData ScientistCompare PCA and Autoencoders Under the Curse of Dimensionality
This question tests your conceptual understanding of dimensionality reduction techniques and your ability to weigh their performance in high-dimensional feature spaces prone to the curse of dimensionality.
Machine LearningTechnical ScreenDimensionality ReductionPCAAutoencoders - C3 AIHardData ScientistCompare Transformers and RNNs and Derive Attention Complexity
Evaluate candidate's deep learning foundations by comparing Transformer and recurrent architectures and deriving exact time/memory complexities for standard scaled dot-product attention across training and inference.
Machine LearningTechnical ScreenTransformersRNNAttention Mechanism - C3 AIHardData ScientistConnect Bias-Variance Trade-offs to Bagging and Boosting
This question tests the conceptual understanding of how ensemble methods manipulate model error components through different structural paradigms.
Machine LearningTechnical ScreenBias-VarianceEnsemble LearningBagging - C3 AIMediumData ScientistDecompress an encoded string
Decompress an encoded string 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 & AlgorithmsOnsite - C3 AIMediumData ScientistDescribe challenge, influence, and mentorship outcomes
This question evaluates leadership competencies for a Data Scientist role, including influence and persuasion, mentoring, prioritization, trade-off reasoning, and the ability to quantify impact through metrics.
Behavioral & LeadershipOnsite - C3 AIEasySoftware EngineerDesign a restaurant reservation system
This question evaluates skills in system architecture and engineering, including data modeling, API design, concurrency control to prevent double-booking, availability and scheduling logic, scalability under traffic spikes, and operational concerns like auditability and low-latency queries.
System DesignOnsite - C3 AIMediumSoftware EngineerEvaluate Factual Accuracy and Consistency in a Domain-Specific RAG Chatbot
Evaluate a rare-disease RAG chatbot using domain consistency and expert review, and distinguish factual accuracy from retrieval metrics and lexical overlap.
Machine LearningOnline Assessment - C3 AIHardData ScientistEvaluate a Retrieval-Augmented Generation System
Design an end-to-end evaluation framework for an enterprise RAG system that assesses retrieval quality, generative fidelity, claim-level attribution, security controls, and production telemetry.
ML System DesignTechnical ScreenRAGLLM EvaluationSystem Design - C3 AIMediumData ScientistExplain Training R-Squared After Adding a Random Feature
Prove that ordinary training R-squared cannot decrease when adding a feature, while adjusted and test-set R-squared can.
Statistics & MathOnline Assessment - C3 AIHardData ScientistExplain an End-to-End ML Project to Mixed Audiences
This question assesses the ability to articulate a technical machine learning project, balancing depth for engineers with accessibility for non-technical stakeholders, while demonstrating rigorous problem-solving.
Behavioral & LeadershipOnsiteBehavioralML LifecycleCommunication - C3 AIMediumData ScientistExplain key ML/stats concepts
Explain key ML/stats concepts evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - C3 AIMediumData ScientistExplain the Gaussian Distribution and Its Limits
Tests a candidate's mathematical understanding of the Gaussian distribution, key theoretical properties such as affine closure and the Central Limit Theorem, and the ability to diagnose and handle non-normal distributions in real-world modeling.
Statistics & MathTechnical ScreenStatisticsProbabilityAnomaly Detection - C3 AIMediumData ScientistExplain the Margin View of Boosting Classification
Understand boosting classification through sequential learners, signed margins, surrogate losses, and the limits of training-margin claims.
Machine LearningOnline Assessment - C3 AIHardData ScientistFind longest substring with at least k repeats
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Find longest substring with at least k repeats states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - C3 AIHardData ScientistFind the Intersection of Two Linked Chains
This problem tests the ability to detect the intersection point of two singly linked lists with constant space complexity, focusing on pointer manipulation and list traversal techniques.
Coding & AlgorithmsOnsiteLinked ListsTwo PointersAlgorithms - C3 AIHardData ScientistForecast Food Stocking Needs Under Waste and Stockout Costs
Design an end-to-end inventory forecasting and stocking optimization system for perishable food items under asymmetric costs of spoilage versus stockouts. The design covers handling censored sales data, probabilistic demand modeling, cost-optimal stocking policies, and validation strategy.
ML System DesignTechnical Screenml-system-designtime-seriessupply-chain
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