⸻ Tree traversal questions were mostly LeetCode medium level, focusing on classic BFS and DFS patterns without heavy backtracking. The emphasis was on implementing standard traversals cleanly, understanding when to use iterative vs recursive approaches, and handling common variations like level-order processing or simple tree properties. The ML and system design portion focused primarily on recommender systems, along with fundamental machine learning theory. This included discussing how to design a recommendation pipeline end-to-end, tradeoffs between different approaches, and demonstrating a solid grasp of core concepts like model evaluation, overfitting, and basic algorithms.

Round 1: Backend Coding(1h) + ML coding(1h) Backend Coding: A non-leetcode question with regard to hashmap and recursion ML coding: Given a dataset, build a pipeline from data preprocessing to model training to evaluation