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Coding questions

Explore algorithm tutorials, coding and system design questions, interview experiences, and computer science study guides.

1,899 resources · Page 25 of 80

  1. Coding questions · Adobe

    Design a nested-list iterator

    This question evaluates the ability to implement an efficient iterator over nested lists, testing skills in iterator state management, lazy traversal, explicit stack usage, and algorithmic complexity reasoning.

  2. Coding questions · OpenAI

    Design a persistent key-value store

    This question evaluates a candidate's ability to design persistent storage and in-memory data structures, covering serialization of arbitrary values, medium/interface design for byte-level persistence, crash-consistent flushing, error handling, and performance and complexity analysis.

  3. Coding questions · Robinhood

    Design a referral leaderboard with chain-based counts

    This question evaluates a candidate's ability to model and compute transitive referral counts using graph-like structures and efficient data structures, testing skills in algorithm design, counting, and sorting within the Coding & Algorithms domain.

  4. Coding questions · Capital One

    Design a reproducible data pipeline for modeling

    This question evaluates a candidate's ability to design reproducible, idempotent data pipelines and feature engineering workflows using SQL and Python, including handling late or out-of-order events, deduplication, backfills, data-quality checks, and orchestration.

  5. Coding questions · Apple

    Design a rolling five-minute hit counter

    This question evaluates understanding of data-structure selection, amortized time complexity, space-time trade-offs, sliding-window state management for expiring events, and the ability to design a concise API with unit tests.

  6. Coding questions · Amazon

    Design a Scalable, Fault-Tolerant Distributed Order System

    This question evaluates system-design and algorithmic competencies, combining distributed-systems concepts — high availability, low-latency architecture, scalability levers, and fault-tolerance mechanisms — with practical binary-tree traversal and time–space complexity analysis.

  7. Coding questions · Anthropic

    Design a single- and multi-threaded web crawler

    This question evaluates skills in web crawling, URL parsing and fragment sanitization, graph traversal for deduplication, and concurrent programming for thread-safe crawling, and it falls under the Coding & Algorithms domain.

  8. Coding questions · DoorDash

    Design a single-machine LRU cache

    This question evaluates a candidate's ability to design an efficient in-memory LRU cache and apply concurrency control, covering eviction semantics, capacity management, key updates, and synchronization for thread safety.

  9. Coding questions · Amazon

    Design a streaming median structure

    This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Design a streaming median structure states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  10. Coding questions · Lyft

    Design a temporal key-value store with historical reads

    This question evaluates a candidate's grasp of data structures and algorithms needed to implement a temporal key-value store supporting timestamped set/get operations and efficient historical reads, including performance targets like O(log n) per operation.

  11. Coding questions · Meta

    Design a time-versioned key-value store and find common free time

    This question evaluates proficiency in designing time-versioned data structures and reasoning about interval-based scheduling, covering competencies in temporal data retrieval, state versioning, and computing common free time across multiple calendars.

  12. Coding questions · StackAdapt

    Design a time-windowed key-value store

    This question evaluates a candidate's ability to design efficient data structures and algorithms for a time-windowed key-value store, focusing on expiration handling, update semantics, and maintaining aggregate metrics under memory constraints.

  13. Coding questions · Experian

    Design Algorithm for Longest Increasing Subsequence Length

    This question evaluates algorithm design and data structure skills, covering subsequence analysis, constraint/graph modeling for seating, and external-memory sorting considerations for scalability.

  14. Coding questions · Pinterest

    Design Algorithm to Minimize Payments in Expense-Sharing App

    This question evaluates algorithm design and optimization skills, focusing on modeling multi-party transactions and minimizing the number of settlement operations.

  15. Coding questions · NVIDIA

    Design algorithms for test scheduling

    This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Design algorithms for test scheduling states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  16. Coding questions · Snowflake

    Design an analytic warehouse for event data

    This question evaluates a candidate's competency in data warehousing and engineering for analytics, including event-level schema and partitioning, slowly changing dimensions, idempotent ingestion and deduplication, derived metrics modeling, and query performance and cost trade-offs using SQL/Python.

  17. Coding questions · Databricks

    Design an efficient Tic-Tac-Toe engine

    This question evaluates a candidate's understanding of efficient algorithm design and minimal state management for a Tic-Tac-Toe engine on an n x n board, including constraints such as O(1) time per move and O(n) space.

  18. Coding questions · Remitly

    Design an Elevator System and Scheduler

    This question evaluates a candidate's object-oriented design and algorithmic scheduling competencies, focusing on concurrency control, fault tolerance, queue management, tie-breaking rules, and time/space complexity analysis within an elevator control system.

  19. Coding questions · Intuit

    Design an idempotent churn ETL pipeline

    This question evaluates a candidate's competence in designing robust ETL pipelines, covering idempotent processing, handling late-arriving streaming subscription events, SCD Type 2 slowly changing dimensions, incremental backfills, data quality checks, upsert/merge semantics, and metric versioning.

  20. Coding questions · Stripe

    Design an idempotent SQL ETL for late data

    This question evaluates practical skills in idempotent ETL design, deduplication by natural keys, handling late-arriving and out-of-order records, upsert/merge strategies, and SQL-based aggregation for daily metrics.

  21. Coding questions · Anthropic

    Design an in-memory banking service

    This question evaluates system-design and algorithmic competencies related to stateful in-memory services, including temporal semantics, merge and re-creation semantics, transactional transfer handling, activity aggregation, and appropriate data-structure and complexity reasoning.

  22. Coding questions · Meta

    Design an in-memory cloud file system

    This question evaluates understanding of object-oriented design, stateful data structures, and resource management including user quotas, ownership semantics, and file metadata operations.

  23. Coding questions · Meta

    Design an in-memory database with TTL and history

    This question evaluates understanding of time-versioned in-memory key–field–value stores, covering TTL-based expiration, historical reads, atomic compare-and-set/delete semantics, efficient scan/scan-by-prefix operations, and time/space complexity analysis, and it belongs to the Coding & Algorithms domain.

  24. Coding questions · Harvey

    Design an in-memory file system with limits

    This question evaluates the ability to design and implement hierarchical in-memory data structures, manage capacity constraints and OS-style duplicate naming, and reason about edge cases and time/space complexity.