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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 121–150 of 3276 results

  • Count four-neighbor islands in a binary matrix and consider the connectivity state needed when rows arrive as a stream.

    AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Count expressions reaching a target sum states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates string-processing and combinatorial counting skills, including tracking distinct-character occurrences across contiguous partitions and using frequency-based pairing logic on arrays to form equal-cost packages.

    AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This question evaluates proficiency in graph traversal and grid-based connected-component detection using depth-first search on a binary m x n grid, along with algorithmic complexity analysis and techniques to avoid revisiting cells.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Count how many structurally different binary search trees can store the keys 1 through n exactly once, for n up to 19. It tests reasoning about how tree shapes decompose and counting combinatorial structures efficiently instead of building every tree.

    AmazonSoftware EngineerCoding & AlgorithmsOther
  • This question evaluates a candidate's ability to implement string transformations based on a fixed character-to-code mapping and to identify distinct results, testing competencies in encoding, string manipulation, and deduplication.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question tests the ability to detect cycles in a directed graph to determine if a set of academic courses with prerequisite constraints can be completed.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Construct one valid binary tree from unordered parent-child descriptions that identify left and right links. Reuse node identities across records, support children appearing before parents, and return the unique root.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Design 2D DP on a DAG 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.

    AmazonSoftware 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 Design LCA with flexible inputs and rule states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates understanding of cache design and frequency-based eviction policies, efficient data structures that support O(1) operations, and techniques for approximate frequency estimation and summarization in distributed streaming environments.

    AmazonApplied ScientistCoding & AlgorithmsOnsite
  • 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.

    AmazonData ScientistCoding & AlgorithmsOnsite
  • This question evaluates understanding of fixed-capacity circular queue data structures, array-based indexing, modular arithmetic, and the requirement for constant-time (O(1)) enqueue/dequeue operations.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite
  • 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 hierarchical locking tree states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Design a no-repeat frequency-based music player with addUserSongs and getNextSong APIs. Covers max-heap data structures, lazy deletion, cycle resets, mid-cycle song additions, and complexity.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to implement efficient weighted random selection using prefix sums and binary search, and handling state updates.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's ability to design efficient array algorithms and appropriate data structures for nearest-smaller-element queries, assessing performance, correctness with duplicates, and scalability to large inputs.

    AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
  • Design rolling-window top-K click tracker 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.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite
  • Tests detection and resolution of cycles in singly linked lists using two-pointer techniques with constant auxiliary space.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates mastery of graph algorithms and graph theory concepts—specifically directed cycle detection—and assesses the ability to reason about algorithmic correctness and to analyze time and space complexity for competing approaches.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates understanding of cycle detection in linked lists and directed graphs, assessing competencies in pointer manipulation, graph traversal strategies, and reasoning about algorithmic time and space complexity within the Coding & Algorithms domain.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in string processing, selection and use of appropriate data structures and algorithms (hashing, tries), dynamic programming and memoization, and complexity analysis for large inputs and edge-case handling within the Coding & Algorithms domain.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates string-processing skills and algorithmic problem-solving, including handling character uniqueness and performance considerations. Commonly asked in Coding & Algorithms interviews to assess a candidate's ability to implement efficient solutions and reason about time/space trade-offs, it targets practical application rather than purely conceptual understanding.

    AmazonData ScientistCoding & AlgorithmsTechnical Screen
  • This question evaluates string-processing and algorithmic problem-solving skills, including handling incremental updates, counting substrings impacted by character replacements, and performance-aware simulation.

    AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This problem tests your ability to model dependencies using a directed graph and detect cycles to determine if a set of tasks can be completed.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to handle interval sorting and linear-time overlap detection to ensure no two meetings conflict.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Tests array manipulation, counting techniques, and greedy or constraint-satisfaction logic to determine if an array can be permuted so no two adjacent elements sum to ten or more.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Determine whether an entire breadth-first encoded binary tree obeys the strict BST invariant. Use ancestor bounds rather than only parent-child checks, reject duplicates, handle numeric extremes, and discuss an iterative approach for deep trees.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to model prerequisites as a directed graph and detect cycles to determine if a valid topological ordering exists.

    AmazonApplied ScientistCoding & 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 Determine string segmentability with dictionary states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite

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