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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 1051–1080 of 3276 results

  • This question tests state management through a Command pattern implementation for undo/redo operations and spatial reasoning using depth-first search (DFS) to identify connected components in a 2D grid.

    FigmaMachine Learning EngineerCoding & AlgorithmsOnsite
  • Determine if a sequence of structural tokens maintains valid scoping rules—never closing an un-opened level and closing all levels by the end. Tests single-pass balance checking, edge-case handling for empty streams, and O(1) space optimization.

    FigmaSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates SQL proficiency—specifically joins, filtering, type casting, temporal aggregation, window functions or deduplication, and deterministic tie-breaking—by requiring derivation of first non-creator view times and per-creator collaborator counts from event tables.

    FigmaData EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests advanced search heuristics and state-space pruning by requiring the implementation of a Wordle-style feedback system that uses a minimax strategy to identify a secret word from a dictionary.

    FlexportSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to manipulate interval data, specifically calculating the total coverage area and finding the maximum concurrency using a sweep-line algorithm.

    FlexportSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to apply dynamic programming to linear structures and tree-based data structures to find the maximum weight independent set under constraints.

    FlexportSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates string processing, strict input validation rules, and constrained combinatorial search skills (e.g., backtracking) for generating and verifying IPv4 addresses.

    FlexportSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's understanding of array-based data processing, difference maximization, and basic algorithmic optimization for time-series inputs. It is commonly asked in Coding & Algorithms interviews to assess fundamental practical programming ability and attention to edge cases (e.g.

    FlipsterSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates competency in implementing and generalizing merge operations on sorted arrays, exercising algorithmic problem-solving, array manipulation, handling of edge cases (empty arrays, negative numbers, duplicates), and analysis of time and space complexity.

    Fora TravelSoftware EngineerCoding & AlgorithmsOnsite
  • Tests array manipulation, handling buffer capacity without relying on placeholder values, and analyzing the trade-offs of in-place sorting strategies.

    Fora TravelSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to manipulate linked list pointers iteratively and perform efficient binary search on a structurally complex matrix.

    FortinetSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates competence in designing panel-aware time-series cross-validation with embargo handling, grouped blocking, deterministic splitting, scalability analysis, and adversarial unit-test design for issues like duplicate timestamps and entity migration.

    Freddie MacData ScientistCoding & AlgorithmsOnsite
  • A coding question that merges an unsorted list of closed integer intervals into disjoint intervals sorted by start. Intervals that overlap or merely touch at an endpoint must be combined, so it tests exact endpoint semantics, transitive merges, and nested or duplicate intervals.

    FurtheraiMachine Learning EngineerCoding & AlgorithmsOnsite
  • This question evaluates understanding of data structures and algorithms for modeling and implementing a simplified spreadsheet, covering dependency graphs, formula evaluation, incremental updates, and cycle detection.

    Fuse EnergySoftware EngineerCoding & AlgorithmsTechnical Screen
  • A matrix coding question: an odd-sized square matrix is filled with a clockwise spiral that starts at the center cell, and you must return the number on its secondary diagonal a given number of rows above or below the middle row. It tests precise index arithmetic and reasoning about how the spiral grows outward ring by ring.

    GarminSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests the candidate's ability to use a stack data structure to track nested states and handle character matching logic.

    Garner HealthSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates algorithmic problem-solving with arrays and visibility relations, assessing competency in array traversal, handling of relative ordering constraints, and designing time- and space-efficient approaches.

    GeSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This pair of problems evaluates algorithmic problem-solving skills, focusing on spatial optimization with Manhattan distances for grid-based demand aggregation and sequential penalty minimization on binary time-series data, emphasizing reasoning about aggregation and trade-offs.

    GeSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests greedy strategies for path optimization and recursive descent parsing techniques for custom data structures.

    GeicoSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's ability to design efficient sliding-window and streaming algorithms for time-series event data, encompassing deduplication, handling bounded out-of-order arrivals and clock skew, distinct-device counting, and meeting specified time and space complexity constraints.

    GeminiData ScientistCoding & AlgorithmsOnsite
  • This problem tests your ability to navigate a 2D grid and identify distinct connected components using graph traversal techniques like Depth-First Search (DFS) or Breadth-First Search (BFS).

    GeneralmotorsMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests the ability to combine a hash map with a doubly linked list to achieve O(1) performance for cache operations.

    Gimlet LabsMember of Technical Staff, ML SystemsCoding & AlgorithmsOnsite
  • Given trains described as time-indexed lists of stations, decide whether a passenger starting at one station at time zero can reach another by riding, waiting and transferring. Tests modeling station and time as search states, handling waits and same-time transfers, and breadth-first search over a time-expanded graph.

    GleanMachine Learning EngineerCoding & AlgorithmsOnsite
  • Given a string of only open and close parentheses, delete the fewest characters so the rest is balanced and return the resulting string, using standard left-to-right matching to define a unique answer. Tests bracket matching, linear-time scanning and careful handling of leftover unmatched characters.

    GleanSoftware EngineerCoding & AlgorithmsOnsite
  • Solve train-schedule reachability with waiting and transfers using time-respecting graph search. The answer models earliest station arrival, preprocesses station visits, handles forward-only train routes, and explains transfer edge cases and complexity.

    GleanMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's ability to model and search time-dependent transit networks, combining temporal graph reasoning, state-space search, and path reconstruction for itinerary reporting.

    GleanMachine Learning EngineerCoding & AlgorithmsOnsite
  • Given a letter grid and a list of words, return every word that can be spelled by a path of edge-adjacent cells without reusing a cell, sorted lexicographically. Covers the single-word search and its many-words follow-up, and tests backtracking and pruning on a large word list.

    GleanSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates proficiency with array manipulation and selection algorithms, specifically understanding order statistics and working with two sorted sequences.

    GleanSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Given two arrays sorted in non-decreasing order and an integer k, return the k-th largest value among all their elements, counting duplicates separately. Tests reasoning about ranks across two sorted sequences, careful handling of empty arrays and duplicates, and moving past a linear scan toward logarithmic time.

    GleanSoftware EngineerCoding & AlgorithmsOnsite
  • Given a list of sentences, treat two words as synonyms when they share the same two words before and two words after, close the relation transitively, and return every synonym group in a canonical sorted order. Tests context hashing, union-find or connected components, and exact output formatting.

    GleanSoftware EngineerCoding & AlgorithmsOnsite

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