I recently had the opportunity to interview with Amazon. The process consisted of a technical round that evaluated both my problem-solving skills and my understanding of modern technologies. In the DSA section, I was asked a question based on partition dynamic programming. The problem required identifying optimal ways to divide a structure (such as an array or string) into segments and applying DP to compute the best result. I approached it by defining a state, exploring all possible partitions, and building a recurrence relation. The interviewer focused on my thought process, clarity in explaining transitions, and time complexity analysis. In addition to DSA, I was also asked several Generative AI theory questions. These included concepts like Large Language Models (LLMs), differences between traditional machine learning and generative AI, and practical ideas such as Retrieval-Augmented Generation (RAG) and hallucinations. The discussion emphasized conceptual clarity and real-world applications rather than deep theoretical details. Overall, the interview experience was positive and well-structured. The interviewer was attentive to my approach and encouraged clear communication throughout the discussion.