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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 331–360 of 3276 results

  • This question evaluates a candidate's ability to design a stateful, multi-level simulation system that processes a time-ordered stream of operations. It tests object-oriented design, incremental complexity management, and handling of deferred effects like scheduled cashback credits. Such object-modeling questions are commonly used in coding interviews to assess practical application of data structures under evolving requirements.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Design and implement an in-memory, timestamped banking ledger supporting atomic transfers, cashback, scheduled payments, account merges with alias resolution, and historical balance lookups.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Design and implement an in-memory cloud storage engine supporting user quotas, file operations, compression/restoration semantics, and glob-based search.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Design an in-memory attendance and payroll tracking system that manages shift lifecycles, deferred rate promotions, overlapping bonus windows, and duration rankings.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question tests your ability to design a concurrent crawler that manages shared state across multiple threads, handles URL de-duplication, and prevents infinite cycles during graph traversal.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to implement an efficient LRU cache using a combination of a hash map and a doubly linked list to achieve constant-time operations.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Implement a robust token usage billing manager that accumulates streaming chunk deltas and non-streaming totals, enforces strict schema and sanity checks, and avoids floating-point precision loss.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • Compute aggregate exclusive execution time for each function from nested start and end events, including recursive calls. Use a stack over a validated single-threaded trace, apply half-open time intervals, and return deterministic sorted results.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's ability to process single-threaded execution logs and compute per-function exclusive durations while reconstructing the call stack at a given timestamp, testing stack simulation, interval reasoning, timestamp tie-handling, parsing of time-stamped events, and algorithmic complexity analysis.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in matrix manipulation, incremental updates, and complexity analysis, specifically maintaining main-diagonal and anti-diagonal sums under point updates.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Convert sampled call stacks to inferred start/end events using ordered common prefixes, recursive frames, explicit final-open behavior, and last-N history limitations.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to process sequential data, apply run-length encoding concepts, and implement filtering logic based on temporal constraints.

    AnthropicMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to manipulate tree-like path structures and manage state transitions using a stack, specifically focusing on efficiently calculating diffs between consecutive call stacks.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question tests the ability to process a stream of state changes, group consecutive identical values into distinct event intervals, and filter these events based on duration.

    AnthropicMachine Learning EngineerCoding & AlgorithmsOnsite
  • Tests the ability to process discrete call-stack snapshots into properly nested, ordered function start and end events using longest common prefix analysis and stack unwinding.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates understanding of sampled stack traces, event reconstruction, handling ambiguous or incomplete profiling data, and algorithmic reasoning about correctness and time/space complexity.

    AnthropicMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates understanding of web crawling and link traversal algorithms, including URL normalization, same-domain filtering, deduplication, and cycle-safe graph traversal.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • 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.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates Python programming proficiency, algorithm design, data structure selection and justification, modular coding practices, unit testing, and time/space complexity analysis.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's competency in file system traversal, robust I/O handling including symbolic links and cycles, content-based deduplication, and algorithmic efficiency within the Coding & Algorithms domain.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates understanding of file-system algorithms, hashing and deduplication techniques, scalable I/O and memory management, and correctness considerations for hash collisions, and it belongs to the Coding & Algorithms domain.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates understanding of cache-efficient, high-throughput hash lookup design and related competencies such as hashing strategies, probe behavior, memory layout, vectorization, bitwise optimizations, prefetching, alignment, and microarchitectural performance trade-offs.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to augment a standard stack with metadata to perform efficient minimum queries without sacrificing the constant-time complexity of stack operations.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates filesystem traversal, efficient I/O and memory use, hashing strategies for content comparison, duplicate-detection algorithms, and time/space complexity analysis.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's competency in algorithm design and data-structure reasoning for detecting n-length consecutive integer sequences, encompassing handling of duplicates, negative values, and dynamic insertions and deletions.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in array processing, run-length detection, algorithmic complexity analysis, and the use of range/suffix data structures within the Coding & Algorithms domain.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's skills in file-system traversal, content deduplication strategies, hashing, large-file I/O handling, concurrency, and robustness to errors and file changes during scans.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates the ability to parse file-system-style input, group files by identical content, and manage deduplication using appropriate data structures and algorithms.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates the ability to design scalable file deduplication algorithms, specifically testing knowledge of hashing strategies, collision handling, memory and I/O optimization, handling very large files, incremental/resumable operation, and complexity and trade-off analysis.

    AnthropicSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates skills in string parsing and content-based grouping for duplicate file detection, along with matrix manipulation for image operations like horizontal flips and box blur, covering competencies in algorithm design, data-structure selection, and numerical array transformations.

    AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen

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