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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 1261–1290 of 3276 results

  • Practice a Google coding interview problem focused on return words matching a typed prefix. The prompt emphasizes edge cases, clean implementation, and verifiable test behavior without revealing the solution.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • Given a binary search tree in level-order form and an inclusive range of values, return every key in the tree that falls inside the range, in ascending order. Tests use of the search tree ordering, unbalanced trees, empty results and inclusive boundary handling.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem evaluates string-processing and filtering skills, focusing on exact prefix matching, case sensitivity, duplicate handling, and edge-case management over arrays of strings. Commonly asked in Coding & Algorithms interviews, it assesses practical implementation ability and understanding of algorithmic complexity at an implementation-level (basic to intermediate).

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Find the lexicographically smallest pair of distinct indices whose array values sum to a target. Success depends on deterministic tie handling, duplicates, missing-result behavior, nonmutation, hash-based performance, and comparison with sorting-based alternatives.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • Design a recurring job scheduler that selects the highest-priority eligible job and enforces cooldown periods measured by subsequent retrieval invocations.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • Tests binary tree traversal combined with streaming pattern matching (such as the Knuth-Morris-Pratt algorithm) to detect substrings across leaf boundaries without buffering the entire text in memory.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates understanding of randomized algorithms, uniform probability distributions over permutations, in-place array manipulation, and time/space complexity analysis within the Coding & Algorithms domain.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's ability to perform simulation-based hypothesis testing by implementing Monte Carlo sampling and computing an empirical two-sided p-value from observed coin-flip counts, emphasizing statistical computation and numerical accuracy.

    GoogleData ScientistCoding & AlgorithmsOnsite
  • This Coding & Algorithms question for a Data Scientist evaluates understanding of randomized algorithms, bit-level representation of real numbers, and probabilistic error and time-complexity analysis, situated at an algorithmic and mathematical abstraction level that requires both conceptual design and quantitative reasoning.

    GoogleData ScientistCoding & AlgorithmsOnsite
  • A Google coding problem that simulates an in-memory file system through a sequence of mkdir, ls, add and read operations, with missing directories created on demand and existing files appended to. It tests tree modeling, path parsing, sorted directory listings and precise operation semantics.

    GoogleSoftware EngineerCoding & AlgorithmsOther
  • This problem tests your ability to manage interval-based resource allocation using a priority queue and a frequency map to track usage statistics.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to manage resources over time using a priority queue and sorting, focusing on handling events and tie-breaking logic for room availability.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to maintain order and perform efficient removals within a sliding window using balanced data structures or heaps.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Maintain sliding-window means after removing the largest k occurrences, with duplicate-aware rebalancing, exact fractions, and bounded streaming state.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Solve minimum eating speed and merge intervals for a new graduate phone screen. The solution covers binary search on answer, integer ceiling division, monotonic feasibility, interval sorting, overlap merging, correctness, complexity, and edge cases.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Implement algorithms for two interview tasks: minimal directory deletion in a hierarchical filesystem using tree traversal, and valid flower placement on a grid using constraint satisfaction.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates skills in grid-based graph traversal and connected-component detection for the island counting task, and combinatorial counting with multiset considerations for the target-word formation task.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This multi-part question evaluates algorithmic problem-solving across selection algorithms, graph/tree traversal, and string processing, testing competencies such as Quickselect behavior and pivot reasoning, computation of node distances in a tree, and prefix-based substring counting.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • Design an online resource scheduler that dynamically allocates compute rooms to incoming streaming sessions and tracks the top-k most active streams by cumulative duration.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to combine graph traversal (Dijkstra's) with greedy interval scheduling to optimize fleet distribution across multiple locations.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • Tests multi-paradigm algorithmic fluency across dynamic programming with auxiliary states, greedy interval coverage, and monotonic deque optimization for bounded subarrays.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates proficiency in parsing and expression evaluation, recursion/stack management, and string-processing for nested function-style arithmetic, alongside graph modeling, geometric distance reasoning, and traversal algorithms for the bomb chain-reaction scenario; the category is Coding & Algorithms.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This question tests the ability to solve two distinct algorithmic patterns: using a prefix sum hash map to count subarrays with a target sum, and using flood fill to determine the size of connected components in a grid.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Validate feature configuration strings using modular polynomial checksums and resolve valid deployment sequences using dependency graph analysis and cycle detection.

    GoogleSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This problem tests your ability to model complex system dependencies as a directed graph and determine the minimum time to complete a set of tasks given sequential constraints.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to manage hierarchical dependencies using topological sorting and perform block-wise aggregate computations on grid-based data structures.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to manage overlapping intervals using a priority queue and calculate the shortest path in a DAG representing dependencies.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to use Fenwick trees for order statistics and the properties of prefix XORs to solve counting problems efficiently.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to perform string-based arithmetic and handle edge cases in digit-by-digit computation.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Implement an ML pipeline optimization engine that calculates critical path latency and peak parallelism for a task DAG, and optimizes streaming checkpoint placement under sliding-window constraints using dynamic programming.

    GoogleMachine Learning EngineerCoding & AlgorithmsOnsite

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