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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.
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Showing 5761–5790 of 9403 results
- Morgan StanleyMediumSoftware EngineerRespond to MS technology behavioral interview questions
This question evaluates motivation, cultural fit, integrity, judgment, communication, and awareness of technology trends by asking about interest in the organization's technology division, an instance of noticing and addressing a problem, and perspectives on near-term industry developments.
Behavioral & LeadershipOnsite - Morgan StanleyMediumData ScientistSolve Market-Risk and Probability Questions
Morgan Stanley data-scientist HR-screen quant set covering four sub-questions: design a WTI/Brent crude stress-testing framework using heavy-tailed marginals and a t-copula for joint tail dependence; the variance of a sum of correlated normal variables and when the sum is normal; the Monty Hall switching probability; and the eight-balls one-heavier puzzle in two weighings. It probes both conceptual understanding and practical application of dependence modeling, scenario generation, and probabilistic reasoning.
Statistics & MathHR Screen - MorningstarEasyData ScientistExplain a stochastic process simply
This question assesses the candidate's ability to intuitively define a stochastic process, differentiate it from static probability models, and articulate fundamental properties like stationarity and the Markov property.
Statistics & MathTechnical ScreenStatisticsStochastic ProcessesTime Series - MotiveHardSoftware EngineerDesign real-time delivery tracking system
An open-ended Motive system-design interview question: design a real-time courier delivery-tracking platform covering mobile GPS publishing, streaming ingestion and map matching, route/ETA computation and multi-stop optimization, the order/assignment lifecycle, live and historical map rendering, scale, privacy, and failure recovery. Includes a combined reference solution and evaluation rubric.
System DesignOnsite - MotiveMediumSoftware EngineerFetch and parse JSON from REST API
A Motive Software Engineer onsite coding exercise: build a small, resilient script from scratch that performs an authenticated HTTP GET against a paginated REST API, parses the JSON, extracts and transforms records, and produces both serialized CSV/JSON output and per-category top-k aggregates. It evaluates HTTP/REST integration, rate-limit and retry handling with backoff, timeouts, malformed-record handling, unit testing, logging, and complexity analysis.
Data Manipulation (SQL/Python)Onsite - MotiveMediumSoftware EngineerImplement Union-Find and track components
This question evaluates understanding and implementation of the Disjoint Set Union (Union-Find) data structure, covering union by rank, path compression, API design for union(a, b), connected(a, b), count(), and analysis of time and space complexity.
Coding & AlgorithmsOnsite - MotiveMediumSoftware EngineerImplement Union-Find for connectivity
This question evaluates understanding and implementation of the Disjoint Set Union (Union-Find) data structure—including path compression and union by rank—and competency in processing online connectivity queries on undirected graphs, categorized under Coding & Algorithms in the graph algorithms and data structures domain.
Coding & AlgorithmsOnsite - MoveworksMediumSoftware EngineerCompute Jaccard similarity between two strings
This question evaluates string tokenization, set-based similarity metrics (Jaccard index) and basic set operations, testing competency in text processing and algorithmic reasoning within the Coding & Algorithms domain and requiring practical application of these concepts.
Coding & AlgorithmsOnsite - MoveworksMediumSoftware EngineerDesign a car rental booking system
This question evaluates understanding of system design competencies including scalability, data modeling, API design, concurrency control, transactional integrity, and caching for a time-based booking domain.
System DesignOnsite - MoveworksMediumSoftware EngineerFind a secret word via match feedback
This question evaluates algorithmic search, pattern-matching, and information-elimination skills in an interactive Mastermind-style word-guessing scenario, testing string comparison and combinatorial reasoning under constrained feedback.
Coding & AlgorithmsOnsite - NVIDIAMediumSoftware EngineerAnalyze and debug Python utilities
This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Analyze and debug Python utilities states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Data Manipulation (SQL/Python)Onsite - NVIDIAHardSoftware EngineerBuild a Jenkins CI for graphics tests
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Build a Jenkins CI for graphics tests states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnline Assessment - NVIDIAMediumSoftware EngineerClarify and Design a Streaming Top-K Tracker
This question tests the ability to design an efficient data structure for maintaining the top-K elements in a high-throughput stream with time-windowing requirements.
Software Engineering FundamentalsTechnical ScreenData StructuresAlgorithmsStreaming - NVIDIAMediumSoftware EngineerCompare Java and Python: Execution, Typing, Concurrency and When to Use Each
An engineering director asks how Java and Python differ and where each language is stronger or weaker. It tests understanding of execution models, static versus dynamic typing, concurrency and the global interpreter lock, ecosystems, and the judgment to recommend a language for a concrete project.
Software Engineering FundamentalsOnsite - NVIDIAMediumSoftware EngineerCompare deep learning framework trends
Compare deep learning framework trends 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 LearningTechnical Screen - NVIDIAEasySoftware EngineerCompute the Final Robot Score
Tests proficiency in using priority queues (heaps) to manage dynamic multisets under iterative reductions. Evaluates the ability to simulate priority-based selection and reason about time and space complexity.
Coding & AlgorithmsTechnical ScreenHeapPriority QueueSimulation - NVIDIAMediumSoftware EngineerConstruct tree from inorder & postorder
This question evaluates understanding of binary tree reconstruction and traversal order relationships, specifically the ability to derive tree structure from inorder and postorder arrays and implement the corresponding algorithm.
Coding & AlgorithmsTechnical Screen - NVIDIAMediumSoftware EngineerCount Distinct Strings With a Hand-Built Hash Table, Then Parallelize It for GPUs
A coding and concurrency question: count the distinct strings in an array with a hash table built from scratch, then keep the count correct when many threads insert at once. It tests hash table design, locks versus atomic compare-and-swap, and how work division and memory layout affect GPU and CPU performance for a million keys.
Software Engineering FundamentalsTechnical Screen - NVIDIAMediumSoftware EngineerCount Server Pairs Relayed Through Each Node of a Weighted Tree
Given a weighted tree of servers and a signal speed, count for every server how many pairs of other servers it can relay: both path lengths must be multiples of the speed and the two paths must share no cable. Tests tree traversal, path-length reasoning and careful combinatorial counting.
Coding & AlgorithmsOther - NVIDIAHardSoftware EngineerCount Substrings with No Repeated Characters
Tests sliding window / two-pointer techniques to count all valid substrings without duplicate characters in linear time.
Coding & AlgorithmsTechnical Screensliding-windowtwo-pointersstrings - NVIDIAMediumData EngineerData Pipeline Reliability, Backfills, and Spark Optimization
Prepare for a NVIDIA software engineering interview question about data pipeline reliability, backfills, and spark optimization. The prompt highlights practical reasoning, edge cases, trade-offs, and validation criteria without revealing the model answer.
Software Engineering FundamentalsTechnical Screen - NVIDIAMediumSoftware EngineerDefine a Git workflow for CI
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Define a Git workflow for CI states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Software Engineering FundamentalsOnline Assessment - NVIDIAMediumSoftware EngineerDefine developer-centric usability metrics
This question evaluates proficiency in analytics and experimentation and the ability to define and operationalize developer-centric usability metrics for both consumer-facing applications and developer tools.
Analytics & ExperimentationTechnical Screen - NVIDIAHardData ScientistDemonstrate cultural fit and sales-oriented leadership
This question evaluates cultural fit, sales-oriented leadership, customer-facing communication, stakeholder influence, conflict resolution, project failure analysis, product–business alignment, and career trajectory for a Data Scientist role.
Behavioral & LeadershipHR Screen - NVIDIAMediumSoftware EngineerDemonstrate software engineering fundamentals
This interview question evaluates engineering fundamentals, trade-offs, examples, pitfalls, and operational implications in a realistic interview setting. A strong answer for Demonstrate software engineering fundamentals states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Software Engineering FundamentalsOnsite - NVIDIAMediumSoftware EngineerDerive MLP shapes and explain PyTorch broadcasting
This question tests the ability to calculate matrix transformation dimensions in a Multilayer Perceptron (MLP) and explains the rules governing PyTorch broadcasting when performing operations between tensors of different shapes.
Machine LearningTechnical ScreenPyTorchLinear AlgebraTensors - NVIDIAMediumSoftware EngineerDescribe model-to-GPU execution pipeline
Describe model-to-GPU execution pipeline 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 - NVIDIAHardSoftware EngineerDesign a Dockerized GPU test pipeline
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Design a Dockerized GPU test pipeline states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnline Assessment - NVIDIAMediumData EngineerDesign a GPU Telemetry Collection and Analytics Platform
Prepare for a NVIDIA system design interview about design a gpu telemetry collection and analytics platform. This question focuses on requirements, architecture, trade-offs, reliability, and follow-up discussion areas.
System DesignTechnical Screen - NVIDIAMediumSoftware EngineerDesign a Key-Value Store, From the Single-Node Storage Engine to Replication
A system design interview asks you to design a key-value store supporting put, get and delete. It tests how you scope consistency and durability, build a single-node storage engine, partition keys across machines, replicate data, and handle node failures, rebalancing and hot keys.
System DesignOnsite
Morgan Stanley
Motive
NVIDIA