Topic
Coding & Algorithms interview questions
3276 Coding & Algorithms questions from 366 companies, with hints, runnable code and solution walkthroughs.
273 easy2539 medium464 hard
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
Most mentioned in interview experiences
- 1.Solve Algorithmic Challenges in Online Coding Assessments15 reports
- 2.Solve LeetCode string and list problems11 reports
- 3.Solve sliding-window and heap problems8 reports
- 4.Implement LRU cache with O(1) ops7 reports
- 5.Implement and Analyze an LRU Query Cache6 reports
- 6.Determine Length of Longest Unique Substring5 reports
- 7.Match-3 Grid: Find Horizontal and Vertical Runs, Then Clear and Drop Cells5 reports
- 8.Design a single- and multi-threaded web crawler4 reports
- 9.Find an Exit in a URL Maze4 reports
- 10.Find deployable slots in a recurring week from allowed and freeze windows4 reports
Counted from 781 candidate interview experiences on Katkat. How we collect and count
Showing 331–360 of 3276 results
This question evaluates a candidate's ability to design a stateful, multi-level simulation system that processes a time-ordered stream of operations. It tests object-oriented design, incremental complexity management, and handling of deferred effects like scheduled cashback credits. Such object-modeling questions are commonly used in coding interviews to assess practical application of data structures under evolving requirements.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteDesign and implement an in-memory, timestamped banking ledger supporting atomic transfers, cashback, scheduled payments, account merges with alias resolution, and historical balance lookups.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteDesign and implement an in-memory cloud storage engine supporting user quotas, file operations, compression/restoration semantics, and glob-based search.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteDesign an in-memory attendance and payroll tracking system that manages shift lifecycles, deferred rate promotions, overlapping bonus windows, and duration rankings.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteThis question tests your ability to design a concurrent crawler that manages shared state across multiple threads, handles URL de-duplication, and prevents infinite cycles during graph traversal.
AnthropicSoftware EngineerCoding & AlgorithmsTechnical ScreenThis problem tests your ability to implement an efficient LRU cache using a combination of a hash map and a doubly linked list to achieve constant-time operations.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteImplement a robust token usage billing manager that accumulates streaming chunk deltas and non-streaming totals, enforces strict schema and sanity checks, and avoids floating-point precision loss.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteCompute aggregate exclusive execution time for each function from nested start and end events, including recursive calls. Use a stack over a validated single-threaded trace, apply half-open time intervals, and return deterministic sorted results.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates a candidate's ability to process single-threaded execution logs and compute per-function exclusive durations while reconstructing the call stack at a given timestamp, testing stack simulation, interval reasoning, timestamp tie-handling, parsing of time-stamped events, and algorithmic complexity analysis.
AnthropicSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates proficiency in matrix manipulation, incremental updates, and complexity analysis, specifically maintaining main-diagonal and anti-diagonal sums under point updates.
AnthropicSoftware EngineerCoding & AlgorithmsTechnical ScreenConvert sampled call stacks to inferred start/end events using ordered common prefixes, recursive frames, explicit final-open behavior, and last-N history limitations.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteThis problem tests your ability to process sequential data, apply run-length encoding concepts, and implement filtering logic based on temporal constraints.
AnthropicMachine Learning EngineerCoding & AlgorithmsTechnical ScreenThis problem tests your ability to manipulate tree-like path structures and manage state transitions using a stack, specifically focusing on efficiently calculating diffs between consecutive call stacks.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteThis question tests the ability to process a stream of state changes, group consecutive identical values into distinct event intervals, and filter these events based on duration.
AnthropicMachine Learning EngineerCoding & AlgorithmsOnsiteTests the ability to process discrete call-stack snapshots into properly nested, ordered function start and end events using longest common prefix analysis and stack unwinding.
AnthropicSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates understanding of sampled stack traces, event reconstruction, handling ambiguous or incomplete profiling data, and algorithmic reasoning about correctness and time/space complexity.
AnthropicMachine Learning EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates understanding of web crawling and link traversal algorithms, including URL normalization, same-domain filtering, deduplication, and cycle-safe graph traversal.
AnthropicSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates skills in web crawling, URL parsing and fragment sanitization, graph traversal for deduplication, and concurrent programming for thread-safe crawling, and it falls under the Coding & Algorithms domain.
AnthropicSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates Python programming proficiency, algorithm design, data structure selection and justification, modular coding practices, unit testing, and time/space complexity analysis.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates a candidate's competency in file system traversal, robust I/O handling including symbolic links and cycles, content-based deduplication, and algorithmic efficiency within the Coding & Algorithms domain.
AnthropicSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates understanding of file-system algorithms, hashing and deduplication techniques, scalable I/O and memory management, and correctness considerations for hash collisions, and it belongs to the Coding & Algorithms domain.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates understanding of cache-efficient, high-throughput hash lookup design and related competencies such as hashing strategies, probe behavior, memory layout, vectorization, bitwise optimizations, prefetching, alignment, and microarchitectural performance trade-offs.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteThis problem tests your ability to augment a standard stack with metadata to perform efficient minimum queries without sacrificing the constant-time complexity of stack operations.
AnthropicSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates filesystem traversal, efficient I/O and memory use, hashing strategies for content comparison, duplicate-detection algorithms, and time/space complexity analysis.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates a candidate's competency in algorithm design and data-structure reasoning for detecting n-length consecutive integer sequences, encompassing handling of duplicates, negative values, and dynamic insertions and deletions.
AnthropicSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates proficiency in array processing, run-length detection, algorithmic complexity analysis, and the use of range/suffix data structures within the Coding & Algorithms domain.
AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen- Find Duplicate FilesMedium
This question evaluates a candidate's skills in file-system traversal, content deduplication strategies, hashing, large-file I/O handling, concurrency, and robustness to errors and file changes during scans.
AnthropicSoftware EngineerCoding & AlgorithmsOnsite This question evaluates the ability to parse file-system-style input, group files by identical content, and manage deduplication using appropriate data structures and algorithms.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates the ability to design scalable file deduplication algorithms, specifically testing knowledge of hashing strategies, collision handling, memory and I/O optimization, handling very large files, incremental/resumable operation, and complexity and trade-off analysis.
AnthropicSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates skills in string parsing and content-based grouping for duplicate file detection, along with matrix manipulation for image operations like horizontal flips and box blur, covering competencies in algorithm design, data-structure selection, and numerical array transformations.
AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen