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The first round was an online Communication and Aptitude Assessment.
The aptitude section consisted of standard questions covering areas such as logical reasoning, quantitative aptitude, and basic problem solving.
The communication section mainly evaluated my ability to read, understand, and communicate effectively. It included areas such as reading comprehension, grammar, sentence formation, and speaking/communication skills.
The second round was an online technical assessment. This round covered multiple areas rather than focusing on only one technology.
The assessment included:
1 DSA problem – I had to solve a programming problem using data structures and algorithms.
1 Frontend question – related to frontend development, particularly concepts around React and JavaScript.
1 RAG implementation question – I had to demonstrate my understanding of Retrieval Augmented Generation and its implementation.
1 Prompt Engineering question – based on designing and improving prompts for an LLM.
AI fundamentals MCQs – questions covering fundamental concepts of Artificial Intelligence.
Cloud fundamentals MCQs – questions covering basic cloud computing concepts and services.
Last updated Oct 11, 2026
The third round was technical interview.
The interview started with my self-introduction, after which the interviewer moved into a detailed discussion about my projects and technical experience. Some questions focusing on RAG pipelines and all.
Since I had selected Python as my programming language for DSA, I was also asked to implement a Python-based programming logic during the interview. The interviewer focused not only on the final code but also on my approach, logic, and explanation of the solution.
After that some basic OOPS principle explanations with examples and then questions related to Cognizant like: