Coding & Algorithms Interview Questions

Practice 4,007 real coding and algorithms interview questions reported from interviews at Meta, Amazon, Google, Uber and Microsoft. The mix follows what these loops actually ask: arrays and strings, hash maps, two pointers and sliding windows, binary search, linked lists, trees and graphs, BFS and DFS, recursion and backtracking, dynamic programming, heaps and priority queues, and the occasional design-a-data-structure round. Roughly four in five are rated medium, which is where most technical screens sit; 477 are hard and cluster in onsite loops. 3,033 of them open in a console where you can run your solution against the test cases in Python, Java, C++ or JavaScript. 2,712 come from Software Engineer interviews, with the rest from Machine Learning Engineer, Data Scientist and Data Engineer loops, and 404 were set as take-home projects rather than live sessions.

4.0k Questions 391 Companies09.28.2026
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Frequently Asked Questions

How difficult are Coding & Algorithms interview questions?
Difficulty spans a wide range: from quick easy warm-ups to multi-stage hard problems that require advanced data structures or algorithmic proofs. Most companies place a majority of live coding rounds at the medium level, with occasional hard questions used to distinguish senior candidates. Interviewers evaluate problem framing, algorithm choice, time/space complexity, clean implementation, and thoughtful testing under time pressure. Expect more conceptual depth at Google, more consistent medium-to-hard screens at Meta, and Amazon to mix coding with leadership signals. Leveling depends on role and seniority: entry-level focuses on fundamentals; senior roles emphasize design, optimizations, and tradeoffs.
Where does Coding & Algorithms appear in a typical interview loop, and which companies weight it most heavily?
Coding & Algorithms is usually the first technical hurdle: it appears in phone/online screens, followed by one or more onsite or virtual coding rounds during the loop. Typical sequences are a technical screen, two to three in-loop coding sessions, then system design or behavioral rounds. Companies that weight this category heavily include Google, Meta, and Amazon, where coding performance often determines whether you progress. Candidates commonly concentrate practice for weeks or months beforehand; most targeting large tech firms prepare intensively for 6–12 weeks, while some spend 3–6 months for deeper mastery or level changes.
How long should I prepare and how should I structure that preparation?
Plan focused, progressive practice over a realistic timeline. A practical structure is an 8–12 week block: weeks 1–2 solidify fundamentals and language fluency; weeks 3–6 target core patterns (arrays, trees, graphs, DP) with timed problem sessions and pattern tagging; weeks 7–9 emphasize mock interviews, end-to-end problem solving, and optimization tradeoffs; final weeks simulate real loops with full-length sessions and targeted weak-point drills. Candidates with less experience may extend to 12–16 weeks. Regularly review mistakes, practice communicating solutions aloud, and include at least one weekly mock interview with peer or coach feedback.
What key subtopics and patterns should I master for Coding & Algorithms interviews?
Master a consistent set of patterns and their variations: arrays and strings (two pointers, sliding window), hash maps and frequency counts, sorting and binary search, recursion and backtracking, dynamic programming, trees and traversals, graphs (BFS/DFS, shortest paths, union-find), heaps and priority queues, and greedy algorithms. Also practice complexity analysis, space-time tradeoffs, and writing bug-resistant code with tests. For higher levels, focus on amortized analysis, advanced graph algorithms, segment trees or Fenwick trees, and system-aware optimizations. Interviewers value reusable problem templates and the ability to generalize patterns to new prompts.
What are standout tips and common pitfalls to avoid in Coding & Algorithms interviews?
Start by clarifying requirements and constraints, then outline your approach before coding. Write a correct, readable baseline solution first, then iterate to improve complexity while narrating tradeoffs. Test with simple and edge cases, and handle nulls, off-by-one, and empty inputs explicitly. Communicate continuously; silence looks like confusion. Avoid premature optimization, overcomplicating solutions, or ignoring interviewer hints. Time management matters: if stuck, discuss alternate approaches and code a partial solution. Finally, practice mock interviews to reduce anxiety and build the habit of clear, structured explanations under pressure.

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