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Coding & Algorithms interview questions

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

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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 1981–2010 of 3276 results

  • This question evaluates proficiency in tree traversal and aggregation techniques, specifically computing subtree sums using depth-first search while handling traversal state and constraints such as negative values and large n.

    NetflixSoftware EngineerCoding & AlgorithmsOnsite
  • Tests bitmask representation of character sets, frequency-based pair counting, and handling edge cases like empty strings.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests the application of the Command design pattern and stack-based data structures to manage reversible operations in an application state.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question tests the ability to track state while iterating through an array to identify the longest contiguous sub-sequence of identical elements.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates concepts in sliding-window algorithms, hash-based duplicate tracking, case-insensitive string handling, and the extension of batch solutions to streaming (online) inputs.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Tests linear scan and two-pointer/state-tracking techniques to find the maximum number of consecutive identical characters in an ASCII string using constant auxiliary space.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Tests sliding window technique, hash map index tracking, and linear-time constraint optimization on string manipulation.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates competency in set operations, grouping via maps/dictionaries, and algorithmic complexity analysis when comparing suffixes of ordered lists.

    NetflixSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This question evaluates a candidate's ability to design and analyze efficient algorithms for detecting longest consecutive incrementing sequences in unsorted integer arrays, testing understanding of appropriate data structures and time/space complexity trade-offs.

    NetflixData ScientistCoding & AlgorithmsOnsite
  • This problem tests your ability to combine a Least Recently Used (LRU) cache with a command-based undo system, applied to a hierarchical tree structure traversal.

    NetflixSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates proficiency with data structures, API design, and system-level concerns by combining timed in-memory caching, command undo/redo semantics, and depth-first traversal of a tree; it falls under the Coding & Algorithms domain and is commonly asked to assess practical implementation skills, correctness under edge cases, and the ability to reason about time and space complexity. The level of abstraction spans practical application—designing clean APIs and implementing core logic—while also requiring conceptual understanding of complexity analysis and background cleanup or state-management patterns.

    NetflixSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates streaming data processing, string tokenization, and frequency-aggregation competencies, focusing on maintaining accurate counts over incremental text inputs using associative data structures.

    NetflixMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in designing efficient in-memory data structures and tree algorithms, specifically time-to-live cache behavior (expiration semantics and accurate unexpired counts) and per-node subtree-height computations for balance reporting, and it falls under the Coding & Algorithms domain.

    NetflixSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates understanding of time-versioned data storage, related data-structure selection, handling of overwrite semantics at equal timestamps, and the ability to reason about correctness and complexity trade-offs.

    NetflixSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to design a cache that respects both memory capacity and item weights, requiring an efficient combination of a hash map and a doubly linked list.

    NetflixData EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in array algorithms and algorithmic optimization, focusing on handling boundary cases, reconstructing index ranges, and meeting strict O(n) time and O(1) extra-space constraints.

    NetflixData ScientistCoding & AlgorithmsOnsite
  • This question evaluates graph algorithm skills (topological ordering and cycle detection) and software design competencies related to command interfaces, undo semantics, and shared-state management.

    NetflixSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to process unstructured text into a searchable index and navigate hierarchical data structures using path patterns.

    NetflixData EngineerCoding & AlgorithmsOnsite
  • This question evaluates understanding and practical implementation of data structures and algorithms for a weighted cache, including weighted eviction policies, use of ordered maps, and time-space complexity analysis.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Given two integer matrices stored as dense lists of rows in which most entries are zero, return their exact matrix product. It tests handling matrix dimensions correctly and exploiting sparsity to skip work on zero entries while still producing every entry of the result.

    NetflixMachine Learning EngineerCoding & AlgorithmsOnline Assessment
  • Tests traversing a binary tree stored in array (heap) layout to compute subtree heights and identify nodes rooted at height-balanced subtrees along with their tree levels.

    NetflixSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to use the sliding window pattern and a hash map to maintain a range of unique elements efficiently in O(n) time.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in graph algorithms, notably topological sorting and cycle detection, as well as algorithmic complexity analysis and handling of dependency constraints.

    NetflixSoftware EngineerCoding & AlgorithmsOnsite
  • Implement a fixed-capacity key-value cache combining per-key TTL expiration with LRU eviction and O(1) average time per operation.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates understanding of sequence processing, uniqueness detection, and the use of auxiliary data structures to track previously seen elements when finding non-repeating contiguous subsequences, applied to show names instead of characters.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question tests the ability to combine sliding window techniques with bit manipulation or frequency counting to identify pairs of substrings that share no common characters.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Implement run-length string compression that replaces each run of repeated characters with the character and its count, returning the original string when the compressed form is not strictly shorter. Tests careful run handling, multi-digit counts, the tie rule, and complexity and edge-case discussion.

    New RelicBackend EngineerCoding & AlgorithmsOnsite
  • Given an integer matrix, find the number of cells on the longest path whose values strictly increase, moving only up, down, left, or right with no diagonal steps and no wrap-around at the edges. Tests grid search and the ability to turn a working first solution into an efficient one.

    New RelicBackend EngineerCoding & AlgorithmsOnsite
  • This problem tests tree traversal techniques on an index-based representation, focusing on handling negative weights and memory efficiency via an iterative post-order approach.

    NewrelicSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests array manipulation skills and the ability to optimize space complexity by using prefix and suffix product passes instead of division.

    NewrelicSoftware EngineerCoding & AlgorithmsTechnical Screen

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