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Coding & Algorithms interview questions

3276 Coding & Algorithms questions from 366 companies, with hints, runnable code and solution walkthroughs.

Questions3276

273 easy2539 medium464 hard

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Coding & Algorithms interview questions by the numbers

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Katkat has 3,276 Coding & Algorithms interview questions from 366 companies; the most come from Amazon (7%), Meta (7%) and Google (6%). By difficulty: 8% easy, 78% medium and 14% hard. The most common round is Technical Screen (52%). Frequent concepts include Algorithms, Data Structures, Arrays and Greedy.

Companies (366)

Difficulty

  • Easy8% · 273
  • Medium78% · 2539
  • Hard14% · 464

Interview rounds

  • Technical Screen52% · 1706
  • Onsite33% · 1080
  • Online Assessment13% · 427
  • HR Screen1% · 37
  • Other1% · 26

Roles

  • Software Engineer80% · 2629
  • Machine Learning Engineer8% · 253
  • Data Scientist7% · 241
  • Data Engineer1% · 45
  • Backend Engineer1% · 26

Frequent concepts

  • Algorithms9% · 302
  • Data Structures7% · 228
  • Arrays5% · 176
  • Greedy5% · 164
  • Optimization5% · 151
  • Hash Map4% · 124

Showing 361–390 of 3276 results

  • This question evaluates the ability to interpret time-ordered profiler event logs, compute inclusive and exclusive function runtimes, reason about call-stack semantics, and recognize anomalies such as recursion, cycles, missing end events, or non-returning functions.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Simulate a virtual machine's instruction set, detect an infinite loop cycle, and find the single instruction modification (between jmp and nop) that allows the program to terminate normally.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Generate function start and end events from timestamped stack samples, including independently debounced frames across consecutive samples. Address transient appearances and absences, changing stack suffixes, shared timestamps, deep stacks, end-of-input behavior, complexity, and online processing.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates algorithmic and systems engineering skills related to batch image processing, parallelization, I/O versus CPU trade-offs, and deterministic output association within the Coding & Algorithms domain.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates competency in concurrent and parallel processing, practical image manipulation with Python libraries (Pillow), resource management for memory and file I/O, robust error handling, and performance measurement.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to design a decorator that memoizes function results using an LRU policy while ensuring the cache survives process restarts via disk serialization.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's ability to design and implement an LRU cache in Python that supports variable-length positional and keyword arguments with canonical, deterministic hashing for semantically equivalent calls and O(1) get/put via a hash map plus a doubly linked list; it falls under the Coding & Algorithms domain and primarily tests practical implementation skills, API robustness, hashing/equality subtleties, and algorithmic complexity guarantees. The persistence follow-up evaluates understanding of serialization and storage design — on-disk formats, versioning, and atomic write strategies — and compares trade-offs between binary pickle and JSON-based serialization in terms of security, compatibility, and performance, reflecting both conceptual design and practical system-level considerations.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's ability to implement an LRU caching mechanism in Python, including decorator design, argument canonicalization for positional args, *args, and **kwargs, handling unhashable inputs, eviction semantics, cache metadata operations (clear, cache_info), and JSON-based persistence and versioning.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to design an in-memory task management system with concurrency-aware state tracking and implement an efficient file-deduplication routine using hashing.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates skills in designing and implementing a stateful in-memory banking system, covering account modeling, transfer lifecycle and expiration handling, balance management, and tracking aggregate outgoing amounts.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Design and implement an in-memory image transformation engine that applies sequential operations (grayscale, brightness scaling, nearest-neighbor resizing) over a batch of images efficiently.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's ability to design and implement an efficient in-memory cache with eviction behavior, testing understanding of data structure selection, state management, and time/space complexity under capacity constraints.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Design and implement a greedy longest-match text tokenizer that validates vocabulary, supports configurable unknown-token coalescing, and emits token IDs alongside their exact substring matches.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Design and implement a concurrent batch image processor that executes a sequence of matrix transforms across worker threads or processes while isolating per-job failures.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Implement an in-memory DNS resolver supporting A and CNAME record lookups, domain name normalization, TTL expiration, and CNAME alias chain cycle detection.

    AnthropicMachine Learning EngineerCoding & AlgorithmsOnline Assessment
  • Design an in-memory task manager supporting per-user task versioning, time-to-live (TTL) expiration, and point-in-time historical queries.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to design a hierarchical file system data structure and implement operations for directory traversal, file manipulation, and path normalization.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to design a multi-threaded web crawler that efficiently traverses a site while respecting domain constraints and avoiding redundant work.

    AnthropicMachine Learning EngineerCoding & AlgorithmsOnsite
  • This question evaluates the ability to design and implement an extensible prefix tree (trie), covering competencies in Unicode-aware string handling, memory and time optimization, concurrency or thread-safety considerations, and support for operations like insert, search, startsWith, countPrefix and erase.

    AnthropicMachine Learning EngineerCoding & AlgorithmsOnsite
  • Implement an in-memory nested key-value store supporting field-level time-to-live (TTL) expiration and snapshot backup/restore operations.

    AnthropicSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This question evaluates a candidate's competence in data structures and algorithms for time-ordered state tracking, time-series indexing, conflict resolution (last-write-wins), memory-efficient storage, and concurrent system behavior under high update rates.

    AnthropicMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Implement crawler, dedup, and persistent LRU states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Implement streaming file deduplication that filters by size, fingerprint and hash, then compares bytes to rule out hash collisions. It covers large-file chunking, batched disk I/O, parallelizing across cores or machines, a dry-run report, and hard links that never cross devices. Follow-ups turn it into a command-line tool that handles symbolic links, unreadable files, error cases, and options to move or delete duplicates.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates skills in graph traversal and cycle detection, hostname-based filtering, URL parsing, and basic network error handling. Commonly asked in Coding & Algorithms interviews, it assesses algorithmic reasoning about traversal strategy and complexity (time and space) analysis while balancing conceptual understanding with practical implementation details.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This set of problems evaluates implementation correctness and attention to formatting for a staircase-printing routine alongside distributed-algorithms reasoning for computing a global mode/median, testing algorithmic thinking, I/O handling, synchronization, and communication-cost trade-offs in the Coding & Algorithms domain.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates competency in concurrent programming, synchronization primitives, and implementation of thread-safe bounded blocking queues within the Coding & Algorithms domain.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Design an in-memory key-value store supporting nested transactions with begin, commit, rollback, get, set, and delete operations.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • LRU CacheMedium

    This question tests the ability to combine a hash map and a doubly linked list to implement an LRU cache with O(1) performance for both get and put operations.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Implement a filesystem path resolution function that normalizes paths containing '.', '..', and symbolic links while detecting cycles.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Implement a breadth-first search crawler that traverses pages starting from a seed URL, filtering for pages sharing the same domain and properly deduplicating visited URLs.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite

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