Okay, here's my interview experience with Amazon for an internship position. Unfortunately, I was rejected after the technical round, which I found to be of medium difficulty. Let me walk you through it. # The Process The interview was technical and started with brief introductions. We then jumped straight into two Data Structures and Algorithms (DSA) questions. The first was based on binary search, specifically searching on answers. I managed to solve it partially but struggled with the edge cases. The second question was about finding the Lowest Common Ancestor (LCA) in a binary tree. I solved it, but my solution was a bit complex. # Topics Covered & Difficulty The interview focused on Data Structures and Algorithms. One question involved a medium-difficulty binary search problem where I had to efficiently search for an optimal solution within a range, but I struggled with some edge cases. The second question was a standard tree problem, specifically finding the Lowest Common Ancestor in a Binary Tree. While I solved it, I made it a bit more complex than necessary. You can find the exact wording of the questions I was asked in the dedicated section below. # My Thoughts on the Culture Amazon is one of the best companies in the world, and it definitely has a great company culture. The environment seemed very focused and driven, with a strong emphasis on problem-solving and innovation. # Advice for Others My main advice would be to write clean, well-documented code and explain your thought process clearly. The interviewer specifically told me to improve my code writing and explanation skills. Even if you solve the problem, poor code quality or a lack of clear communication can be a reason for rejection. Focus on making your code easy to understand and articulate your reasoning behind each step. Good luck!

I'm thrilled to say that I was selected in Amazon. There was a résumé shortlisting round, and after my résumé got selected, my interview was scheduled. # Interview Process There were two in-person interviews. ## 1. DSA Round This round focused on problem-solving and core data structures. I was asked two medium–hard level questions: One was a graph traversal question with a DP twist, which made it slightly tricky. The second was based on 1-D Dynamic Programming. ## 2. ML Breadth & Depth Round This round covered a wide range of Machine Learning concepts. Topics included: Self-attention mechanism Transformer architecture (encoder and decoder) Deep learning training process Classical ML algorithms Towards the end, I was also asked conditional questions on overfitting and underfitting, which tested conceptual clarity. Overall, I would rate the difficulty as medium to hard. # Advice for Others Practice 2–3 DSA questions daily and stay consistent. Build strong conceptual understanding of ML algorithms and architectures. Study the concepts of overfitting and underfitting in depth.