Amazon

I applied for the Software Development Engineer Intern role at Amazon through the off-campus process. The internship was a six-month opportunity, and the hiring process started with an online assessment followed by a technical interview. The online assessment had multiple sections. There was a debugging section containing seven code snippets, where I had to identify and fix the issues within the given time. The next section was the coding round. There were two coding questions. One was based on a tree and the other involved arrays. Both were around the easy-to-medium range, but time management was important because the assessment had several other sections as well. The OA also included a behavioral/work-style section and a reasoning section. The behavioral questions were based around workplace situations and required choosing the response that best matched how I would handle a particular situation. The reasoning section contained aptitude and verbal reasoning questions. After clearing the assessment, I received the interview invitation. The interview process consisted of elimination rounds, so each stage was important. Technical Interview The technical interview started with a short introduction. The interviewer then moved directly into DSA. The first problem was related to trees. I was asked to find the bottom and top views of a binary tree. I explained my approach using tree traversal and discussed how the nodes could be associated with their horizontal distances. The interviewer asked follow-up questions about the approach and wanted me to consider different edge cases. I then implemented the solution and discussed its time and space complexity. The second problem was based on a rotated sorted array. I first considered a straightforward approach, but the interviewer asked me whether the problem could be solved more efficiently. I then moved towards a binary-search-based solution. The important part was identifying which half of the array was sorted and deciding where the target could lie. I explained the reasoning before implementing the solution. The interviewer also asked questions about my projects. We discussed the technologies I had used, the architecture of one of my projects, and some of the implementation decisions I had made. The interviewer was particularly interested in understanding what I had personally contributed to the projects rather than simply hearing a description of the application. There were also some questions related to Amazon Leadership Principles. I was asked about situations where I had faced a difficult problem, made a decision, or worked through a challenge. I tried to structure my answers around the situation, my actions, and the result. The interview was approximately an hour long. The main focus was still DSA, but the project and behavioral discussion were also important. Unfortunately, I was not selected after the interview process. Looking back, the biggest takeaway was that Amazon's internship process requires preparation beyond just solving coding problems. The OA included debugging and behavioral sections, while the interview combined DSA, project discussion and Amazon's Leadership Principles. For preparation, I would focus heavily on trees, graphs, arrays, binary search, recursion, dynamic programming and other common DSA patterns. It is also useful to practice debugging existing code because the OA can contain a dedicated debugging section. I would also prepare the Amazon Leadership Principles and keep several real examples ready from projects, college activities, competitions or internships. The answers should be based on actual experiences rather than memorized statements. Recent Amazon India reports show similar variation in the exact process: some 2025 off-campus candidates had two DSA problems in the OA followed by one technical interview, while other drives used additional interview rounds. Overall, I couldn't convert the Amazon interview into an internship, but the experience helped me understand that Amazon evaluates both coding ability and how clearly a candidate explains their approach, handles follow-ups, and discusses real experiences.