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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 2191–2220 of 3276 results

  • A stock trading optimization problem: given daily prices, find the maximum profit using at most two buy-then-sell transactions while holding at most one share at a time. It tests reasoning about splitting the price history into non-overlapping trades and handling falling, flat or steadily rising prices.

    OracleSoftware EngineerCoding & AlgorithmsOther
  • This problem tests graph traversal techniques (BFS/DFS) and Disjoint Set Union (DSU) to group connected entities based on shared attributes.

    OracleSoftware 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 Minimize sum with halving operations states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    OracleSoftware EngineerCoding & AlgorithmsOnsite
  • A coding question that asks for the minimum number of rooms needed so that every meeting in an unsorted list of half-open time intervals can be held without overlap. It tests reasoning about overlapping intervals, the boundary rule for meetings that end and start at the same time, and an efficient solution for large inputs.

    OracleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to manage temporal data using efficient data structures, specifically focusing on handling expiration logic and range-based queries.

    OracleSoftware EngineerCoding & AlgorithmsOnsite
  • A coding problem that asks you to rearrange an integer array into the next lexicographically greater arrangement of its values, wrapping around to ascending order when the array is already the largest. It tests reasoning about lexicographic order, repeated values and complexity analysis.

    OracleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to manipulate singly linked list pointers in-place, specifically managing alternating node removals while maintaining correct connectivity.

    OracleSoftware EngineerCoding & AlgorithmsOnsite
  • Given a lowercase string and an integer k, repeatedly delete k adjacent equal characters, letting the remaining parts join and possibly form new runs, until no deletion is possible, then return what remains. It tests efficient simulation of cascading removals on strings of up to 100,000 characters.

    OracleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Given two singly linked lists and two indices i and j, replace nodes i through j of the first list with every node of the second list and return the new head. It tests careful pointer relinking, head and tail edge cases, and an empty replacement list.

    OracleSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates understanding of graph algorithms and dependency resolution, specifically skills in cycle detection and producing valid orderings of tasks.

    OracleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests proficiency with hash maps for frequency counting and custom sorting logic using priority queues or sorting algorithms.

    OracleBackend EngineerCoding & AlgorithmsOnsite
  • Find the length of the shortest contiguous subarray of positive integers whose sum reaches a target when each distinct value is counted only once. A coding-screen problem that tests reasoning about contiguous ranges, bookkeeping for repeated values, and correct handling of the case where no subarray qualifies.

    OracleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to maintain data access recency and perform cache evictions in O(1) time complexity using a combination of a hash map and a doubly linked list.

    OracleSoftware EngineerCoding & AlgorithmsOnsite
  • Implement and simulate a least-frequently-used cache with fixed capacity that evicts the key with the lowest use count, breaking ties by least recent use. It tests exact eviction semantics, treating updates and reads as uses, and supporting every get and put in constant average time.

    OracleSoftware EngineerCoding & AlgorithmsOther
  • This set tests proficiency across five key algorithmic domains: graph theory, array manipulation, numerical properties, cache design, and tree traversal.

    OracleSoftware EngineerCoding & AlgorithmsOnsite
  • This multi-part question evaluates proficiency with core data structures and algorithms — specifically trie-based string indexing and autocomplete design, sliding-window/prefix-sum optimization for maximizing a one-shot decrypted range, and interval merging — within the Coding & Algorithms domain, emphasizing practical implementation, complexity analysis and edge-case handling. It is commonly asked in technical interviews to assess the ability to design efficient data structures and linear-time algorithms, reason about time/space trade-offs and ordering constraints, and demonstrate both conceptual understanding and practical application through clear complexity analysis.

    OracleSoftware EngineerCoding & AlgorithmsOnsite
  • Tests string partitioning, palindrome verification, and algorithmic optimization using dynamic programming or two pointers.

    OracleBackend EngineerCoding & AlgorithmsOnsite
  • Given a binary tree of 0 and 1 bits in level-order form, return the sum of the binary numbers spelled by every root-to-leaf path, reading the root as the most significant bit. The problem tests tree traversal, carrying an accumulated value down each path, and parsing a level-order serialization correctly.

    OracleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • A coding question that processes set and get operations on a key-value store where each write carries a timestamp and each read asks for a key's value as of a given time. It tests per-key version history, finding the latest write at or before a query time, and handling many interleaved operations efficiently.

    OracleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests spatial data processing and efficient set operations to identify gaps in service coverage between clients and providers based on distance and specialty requirements.

    OscarSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates string encoding/decoding and combinatorial parsing skills (Morse code conversion and ambiguous decoding) alongside spatial search and filtering competencies for proximity-based access checks, testing algorithmic reasoning, pattern recognition, backtracking possibilities, distance computation, and data-structure use.

    OscarSoftware EngineerCoding & AlgorithmsOnsite
  • Determine whether a very large non-negative integer given as a decimal string is divisible by 8 without converting the whole string to an integer. The key insight is that only the last three digits matter (since 1000 is a multiple of 8), giving an O(1) check; a streaming modular-remainder approach is the O(n) alternative.

    OtherData ScientistCoding & AlgorithmsTechnical Screen
  • This question evaluates string processing and algorithmic optimization skills, specifically handling simulated deletions (backspace behavior) and performing comparisons under a strict auxiliary space constraint.

    OtherBackend EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates array manipulation and numeric computation skills—specifically identifying the maximum element, applying a percentage discount, and correctly handling fractional results and flooring—within the Coding & Algorithms domain.

    OtherSoftware EngineerCoding & AlgorithmsOnline Assessment
  • Given an array of student exam scores as percentages, count how many students failed with under 50, passed with 50 to 80 inclusive, or passed with distinction above 80, and return the three counts in order. It tests careful handling of inclusive and exclusive boundaries and empty input.

    OtherSoftware EngineerCoding & AlgorithmsOnline Assessment
  • Count monotone grid paths that use exact numbers of right and up moves without ever taking three identical moves in a row. Address empty and one-direction paths, return an exact count, and explain how the limits affect time and space costs.

    OtherSoftware EngineerCoding & AlgorithmsOnline Assessment
  • Given the reading time available on each day and the time each chapter of a book takes, find the fewest days needed to read every chapter in order when a chapter can never be split across days, or report that the book cannot be finished. It tests simulation with per-day capacity, ordering constraints, and impossible cases.

    OtherSoftware EngineerCoding & AlgorithmsOnline Assessment
  • Filter structured logs by an ordered minimum severity, preserving threshold-and-higher records while handling empty and unknown levels under an explicit contract.

    OtherSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates understanding of the k-Nearest Neighbors algorithm, distance metrics (Euclidean and cosine), tie-breaking with class priors, weighted voting, complexity analysis, and scalability strategies, testing competencies in algorithm implementation, model behavior, and computational trade-offs.

    OtherData ScientistCoding & AlgorithmsOnsite
  • This question evaluates understanding of linear regression, mean squared error loss, gradient computation, parameter updates, and proficiency with vectorized numerical operations used in batch gradient descent.

    OtherMachine Learning EngineerCoding & AlgorithmsOnsite

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