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This study has been Bloom's Taxonomy is used for educational learning objectives that educators assign to students. During a written examination, the purpose of this taxonomy is that, it is used to validate a student's cognitive level. Educators sometimes find it difficult to determine whether their assessment questions meet the Bloom's taxonomy's requirements at various cognitive levels. Based on this taxonomy, this study suggests an automated analysis of exam questions to find the proper category. This rule-based approach uses Natural Language Processing (NLP) approaches to find essential keywords and verbs that can help identify a question's category. This project focuses on the field of computer programming. The study currently employs a set of 100 questions (70 training questions and 30 test questions). According to preliminary findings, the guidelines may be useful in correctly identifying the Bloom's taxonomy category in exam questions. We've developed a new algorithm that provides complete randomization of questions and eliminates duplicates. Many educational institutions and non-profit organizations will benefit from the suggested approach