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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 2731–2760 of 4616 results
- NVIDIAHardSoftware EngineerFetch Paginated Medical Records and Filter by Field Rules and Historical Average Weight
A practical coding exercise: page through an HTTP endpoint that returns medical records 10 at a time, then return the records that pass a list of field comparisons and a weight condition based on the patient's historical average weight. It tests pagination, robust parsing, per-patient aggregation and working quickly from a long specification.
Software Engineering FundamentalsTechnical Screen - NVIDIAMediumSoftware EngineerFind Top K Frequent Elements
This question evaluates a candidate's proficiency with frequency counting, selection techniques, use of appropriate data structures, and algorithmic time and space complexity analysis.
Coding & AlgorithmsTechnical Screen - NVIDIAMediumSoftware EngineerFind minimum time to cross bridge with flashlight
This problem tests your ability to model a greedy strategy and handle state transitions under constraints to minimize the total time taken for a group to cross a bridge.
Coding & AlgorithmsTechnical ScreenGreedySortingOptimization - NVIDIAEasySoftware EngineerFind the nth prime number
This question assesses algorithmic problem-solving and number-theoretic computation skills, requiring efficient prime generation and complexity analysis in the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - NVIDIAMediumSoftware EngineerHash-Partition an Array on the GPU in C++ and Return Partition Offsets
A GPU programming question that asks you to hash-partition an array of keys into P partitions in CUDA C++, making each partition contiguous and returning each partition's starting offset. Follow-up questions test GPU optimization: atomic contention, memory coalescing, shared memory use and the number of passes over global memory.
Software Engineering FundamentalsTechnical Screen - NVIDIAMediumApplied ScientistHow AI Agents Fit Into Your Research Workflow: Ideas, Experiments, and Paper Writing
A research internship interview question about how AI agents change a researcher's daily workflow across generating ideas, running experiments, and writing papers. It tests concrete delegation choices, how agent output is verified, awareness of failure modes such as fabricated references, and judgment about confidentiality and reproducibility.
Behavioral & LeadershipTechnical Screen - NVIDIAMediumSoftware EngineerHow would you optimize large-scale training/inference?
This problem tests your understanding of performance bottlenecks in large-scale machine learning systems, requiring you to prioritize optimization strategies across memory, compute, and communication dimensions.
ML System DesignTechnical ScreenML SystemsGPU ArchitecturePerformance Engineering - NVIDIAMediumSoftware EngineerImplement 2D convolution using NumPy slicing
This question evaluates a candidate's proficiency with array manipulation and vectorized numerical computation using NumPy, focusing on 2D convolution concepts such as slicing, broadcasting, and filter application.
Data Manipulation (SQL/Python)Technical Screen - NVIDIAMediumSoftware EngineerImplement and Explain a C++ Smart Pointer
Tests understanding of C++ memory management, RAII, explicit ownership semantics, and the Rule of Five by implementing a reference-counted shared smart pointer.
Software Engineering FundamentalsTechnical ScreenC++Memory ManagementRAII - NVIDIAMediumSoftware EngineerIntroduce yourself for a senior role
This question evaluates leadership, communication, ownership, and impact-articulation skills for a senior software engineer, focusing on the ability to concisely present scope, technical and business outcomes, and future alignment.
Behavioral & LeadershipTechnical Screen - NVIDIAMediumSoftware EngineerMarket a new AI technique
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 Market a new AI technique states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - NVIDIAMediumData EngineerMerge Overlapping Time Intervals
Practice a NVIDIA coding interview problem focused on merge overlapping time intervals. The prompt emphasizes edge cases, clean implementation, and verifiable test behavior without revealing the solution.
Coding & AlgorithmsTechnical Screen - NVIDIAMediumSoftware EngineerPlan discovery and adoption strategy
This question evaluates a candidate's skills in product discovery, stakeholder alignment, technical communication, and developer adoption strategy for integrating a new ML technique, including defining use cases, constraints, baselines, and success criteria.
Behavioral & LeadershipTechnical Screen - NVIDIAMediumSoftware EngineerReason About Endianness, Ring Buffers, and Stack Behavior in Firmware
Tests firmware-level engineering concepts across low-level binary data parsing, lock-free ring buffer design for ISR-to-thread communication, and static/runtime stack depth analysis under nested interrupts.
Software Engineering FundamentalsTechnical ScreenFirmwareEmbedded SystemsConcurrency - NVIDIAMediumSoftware EngineerReconstruct tree from inorder and postorder
This question evaluates understanding of binary tree traversal relationships, recursive construction techniques, and algorithmic time and space complexity, reflecting core data structures and recursion competencies.
Coding & AlgorithmsTechnical Screen - NVIDIAEasySoftware EngineerReturn all file paths via DFS
This question evaluates understanding of tree traversal and depth-first search along with competence in handling hierarchical data structures and constructing full file paths.
Coding & AlgorithmsTechnical Screen - NVIDIAMediumSoftware EngineerSolve a Difficult Problem and Navigate Disagreement
Assesses technical problem-solving rigor, hypothesis testing, and the ability to resolve technical disagreements using data, empathy, and shared goals.
Behavioral & LeadershipTechnical ScreenBehavioralConflict ResolutionProblem Solving - NVIDIAMediumSoftware EngineerSolve anagram grouping and in-place allocator
This problem tests string hashing/sorting techniques for grouping anagrams and the ability to manage fixed-size memory blocks in-place without auxiliary data structures.
Coding & AlgorithmsTechnical ScreenHash MapsString ManipulationTwo Pointers - NVIDIAMediumSoftware EngineerSolve unique triplets summing to target
Solve unique triplets summing to target 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 & AlgorithmsTechnical Screen - NVIDIAEasySoftware EngineerWrite SQL to sum city population by name
Assesses SQL proficiency in relational joins and aggregation by asking for a total population computed from ZIP-level data and evaluates reasoning about duplicate entity names.
Software Engineering FundamentalsTechnical Screen - Nash AIMediumSoftware EngineerDesign OOD for probabilistic Minesweeper
Design OOD for probabilistic Minesweeper 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 - Nash AIMediumSoftware EngineerDesign real-time delivery tracking with async updates
This question tests the ability to design a high-throughput, real-time location tracking system that ensures eventual consistency and correct state representation despite network latency and duplicate events.
System DesignTechnical ScreenScalabilityEvent-DrivenCaching - Nash AIMediumSoftware EngineerImplement Minesweeper click and print
Implement Minesweeper click and print 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 & AlgorithmsTechnical Screen - Nash AIMediumSoftware EngineerImplement Minesweeper click-to-reveal expansion
This problem tests your ability to implement recursive traversal (flood-fill) on a 2D grid and manage game state transitions based on player input.
Coding & AlgorithmsTechnical ScreenDFSBFSMatrix - NatooraMediumData AnalystDesign a 25,000-CSV ETL pipeline
This question assesses the ability to design a robust, scalable ETL architecture capable of processing large volumes of semi-structured data while ensuring data integrity and operational reliability.
System DesignTechnical ScreenETLData EngineeringData Quality - NatooraMediumData AnalystExplain pricing fit and ETL architecture
A Natoora Data/Pricing Analyst technical screen that probes how a candidate with no direct pricing experience defends their fit: transferable analytics skills, a ramp plan on food-pricing metrics (revenue, margin, volume, markdown, spoilage), Google Sheets vs code trade-offs, and an end-to-end ETL pipeline deep dive. The model answer shows how to articulate data infrastructure, automation levels, and decision points credibly.
Behavioral & LeadershipTechnical Screen - NatooraMediumData AnalystHow adapt to Google Sheets?
This question evaluates the candidate's ability to transfer core analytics skills to a spreadsheet-first workflow, adapt to collaborative and audit-sensitive data processes, and demonstrate leadership around governance, scalability, and the downstream impact of pricing decisions.
Behavioral & LeadershipTechnical Screen - NatooraMediumData AnalystWhen use LLMs for reporting?
This question assesses the architectural trade-offs, reliability concerns, and suitability of using LLMs for automated business reporting tasks compared to traditional deterministic methods.
Machine LearningTechnical ScreenLLMRAGReporting - NatooraMediumData AnalystWhy use LLMs for daily summaries?
This question evaluates a candidate's ability to justify tooling and architecture choices for automated daily summaries, comparing LLM-based natural language generation with rules-based or templated alternatives while accounting for data sources, consumer intent, validation, failure modes, and cost/latency trade-offs.
Machine LearningTechnical Screen - NatwestEasyProduct ManagerAnswer NatWest behavioral prompts
Prepare NatWest internship behavioral answers for program motivation, commitments, diverse teamwork, decision-making, embracing opportunities, and staying current on technology. The solution uses STAR examples, finance-tech interest, ownership, curiosity, and reflection.
Behavioral & LeadershipTechnical Screen
NVIDIA