Swati maps 2,000+ FAANG questions
A public analysis mapped over 2,000 FAANG interview problems to 99 patterns, showing company biases — Google skews graphs, Amazon favors sliding window/greedy, Meta leans trees/DP, Microsoft focuses on strings/matrices. The breakdown highlights the 15 most common company‑specific problem types you should prioritize in DSA study. (x.com)
Swati Ahuja previously published a public, pattern‑curated LeetCode sheet that organized roughly 400+ problems into 94 core patterns and shared a Google Sheets link alongside a Dev.to writeup dated Oct 2, 2025. (dev.to)) A separate X thread publicly mapped 2,000+ FAANG interview problems into 99 algorithmic patterns and reported company skews — Google toward graph problems, Amazon toward sliding‑window and greedy problems, Meta toward trees and dynamic programming, and Microsoft toward strings and matrix problems. (x.com)) The Dev.to post and linked sheet tag problems by company, pattern, and difficulty, enabling the per‑company frequency counts that underlie the stated “company bias” breakdowns. (dev.to)) Independent analyses and prep outlets have produced similar company‑level pattern profiles—Codeintuition published a comparative “pattern profile” for Google vs. Amazon vs. Meta, and several community repos maintain company‑tagged problem lists used for exacting frequency analysis. (codeintuition.io)) The public dataset’s columnar layout (problem → pattern → company → difficulty) mirrors established pattern collections and tooling such as the LeetCode Patterns web index and the large GitHub pattern repo maintained by seanprashad, both of which are updated regularly and used for targeted, company‑specific study. (leetcode-patterns.vercel.app)) The X thread’s visual breakdown singles out a 15‑item shortlist of the most frequent company‑specific problem types (the “15 to prioritize”), and it links back to the full sheet so engineers can filter by employer and drill into exact problem counts. (x.com))
