During my recent experience with technical interviews, I encountered a mix of moderate level questions and basic coding questions, which included SQL, NumPy, pandas, sklearn, matplotlib, pyplot, and scikitlearn. The interview process involved delayed scheduling of rounds, and there was no follow-up at times, although everything was updated on the portal, even if it could be slow. Most of the questions tested fundamental programming skills and data manipulation techniques, especially with trees and forest algorithms. I also faced practical tasks involving chatbot development, LLM integration, and working with chat models. Understanding concepts related to ANN, CNN, and RNN proved crucial, as the evaluators were keen on assessing both theoretical knowledge and implementation skills. While the interview flow was not entirely seamless, it highlighted the importance of hands-on experience with data analysis libraries and machine learning frameworks. Preparing for these rounds required revisiting coding basics, mastering data structures, and practicing model building with Python tools. Overall, the process was challenging yet insightful, emphasizing that consistent practice and familiarity with libraries such as NumPy, pandas, matplotlib, and sklearn can significantly improve problem-solving efficiency and performance during technical interviews.