Katkat

Topic

Coding & Algorithms interview questions

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

Questions3276

273 easy2539 medium464 hard

Your progress0/3276

Coding & Algorithms interview questions by the numbers

Last updated

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 211–240 of 3276 results

  • This problem tests proficiency with balanced binary search trees or interval management techniques to efficiently track and manipulate sets of half-open intervals.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Tests implementation of a binary min-heap from scratch, including array-based representation, sift-up, and sift-down operations, without relying on built-in priority queue libraries.

    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 Implement a formatted shell script output states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates competency in concurrent systems, scalable data ingestion, and algorithmic design for a high-throughput web crawler, covering URL normalization, deduplication, per-host rate limiting, fault-tolerant checkpointing, and bounded-memory queueing.

    AmazonData ScientistCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in POSIX-compliant shell scripting, including positional argument parsing and validation, ISO 8601 UTC timestamp generation, file metadata extraction for license determination, construction of user@hostname, error handling and exit-code semantics, and handling of non-ASCII file paths.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Implement ad matching and delivery routing 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 question evaluates algorithm design skills and complexity analysis for merging multiple sorted sequences, measuring proficiency with appropriate data structures, time-space trade-offs, and correctness reasoning.

    AmazonData ScientistCoding & AlgorithmsOnsite
  • Implement a fixed-capacity least-recently-used cache for integer query results with expected constant-time get and put operations. Refresh recency on hits and updates, evict the correct key on overflow, and handle misses and duplicate puts precisely.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates skill in parsing and evaluating arithmetic expressions with integer operands and the operators +, -, *, and /, focusing on operator precedence and evaluation order within the Coding & Algorithms domain.

    AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
  • Implement lower_bound and upper_bound on a non-decreasing sorted array using iterative binary search in O(log n) time and O(1) space. This Amazon ML engineer screen tests precise boundary handling, loop-invariant reasoning, and edge cases like duplicates, empty arrays, and out-of-range targets.

    AmazonApplied ScientistCoding & AlgorithmsTechnical Screen
  • This question evaluates implementation-level mastery of transformer architectures, including multi-head masked self-attention, position-wise feed-forward networks, residual connections, and precise tensor-shape reasoning for a decoder-only language model, within the Coding & Algorithms domain of deep learning and neural network engineering.

    AmazonApplied ScientistCoding & AlgorithmsOnsite
  • This question evaluates proficiency in implementing low-level integer arithmetic and algorithmic optimization, including correct handling of overflow and edge cases within 32-bit signed integer constraints.

    AmazonApplied ScientistCoding & AlgorithmsOnsite
  • Implement inventory allocation with backorders 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.

    AmazonApplied ScientistCoding & AlgorithmsOnsite
  • This question evaluates a data scientist's ability to implement efficient multiset intersection and streaming top-k word-frequency computations, covering duplicate-aware counting, dense-ranking, Unicode handling, and strict time/space constraints.

    AmazonData ScientistCoding & AlgorithmsOnsite
  • This question evaluates a developer's ability to implement debounce and throttle utilities in JavaScript, testing competencies in asynchronous timing, timer management, cancellation/flush semantics, error propagation, and handling edge cases such as rapid bursts, timer drift, and re-entrancy.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates competence in streaming algorithms and resource-constrained algorithm design, specifically implementing a k-way merge over blocking, potentially unbounded integer iterators while preserving stable duplicates and reasoning about time and O(k) space complexity.

    AmazonData ScientistCoding & AlgorithmsOnsite
  • This question evaluates understanding of binary tree traversal techniques and algorithmic reasoning for producing per-depth left and right views, assessing competencies in data structures, traversal ordering, and correctness across edge cases.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates algorithm design, data structure selection, and complexity analysis by requiring implementations for minimizing total merge cost and for removing a contiguous block to minimize the sum of adjacent absolute differences.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite
  • Implement a tiered inventory allocation system that distributes limited stock among buyer bids based on price tiers and round-robin priority rules.

    AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
  • Design a request queue where each play takes the song with the most pending requests, with ties going to the most recent request. Tests keeping frequency and recency together so both operations run in O(1).

    AmazonSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's ability to articulate programming language trade-offs, understand language-specific runtime behaviors (types, hoisting, closures), and justify tool choice for front-end coding tasks within the Coding & Algorithms domain.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests tree traversal techniques, the ability to validate binary tree properties recursively, and the use of selection algorithms to find order statistics.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to model hierarchical data using tree traversal and string manipulation to aggregate cumulative metrics across nested domains.

    AmazonSoftware EngineerCoding & AlgorithmsOnsite
  • Tests finding the lowest common ancestor of two values in a Binary Search Tree represented as a level-order serialized list, accounting for self-ancestorship and missing-node edge cases.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Disjoint segments [l, r, v] give every bag from position l to r the value v, and all other bags in an endless row are empty. Find the largest total over k consecutive bags, returned modulo 1,000,000,007, when k and positions reach 10^9, testing window sums over sparse ranges that cut through segments.

    AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This problem tests your ability to efficiently manage interval operations and sliding window logic on a sparse coordinate system.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to solve a variation of the 0/1 Knapsack problem where items must be selected to maximize total value without exceeding a specific budget constraint.

    AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to optimize resource pairing using greedy strategies or sorting to maximize total capacity under specific pair-wise constraints.

    AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This question evaluates algorithm design and optimization skills in resource allocation under constraints, focusing on decision-making to maximize credits when turn-taking and limited skips affect processing.

    AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This question evaluates algorithmic reasoning in constrained pairings and resource allocation, focusing on maximizing aggregate memory under pairwise feasibility constraints while managing large input sizes.

    AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment

More topics