About this previous year paper
This document contains the complete and original B.Tech (Semester II) Theory Examination Question Paper for Artificial Intelligence, designed for the 2023–24 academic session. The paper is structured to evaluate a student’s foundational and advanced understanding of Artificial Intelligence concepts such as machine learning, deep learning, natural language processing, computer vision, neural networks, speech recognition, ethical concerns in AI, and real-life AI applications.
The question paper is divided into three major sections, each designed to test a different level of learning—from basic definitions to analytical reasoning and long descriptive answers.
SECTION A – Short Conceptual Questions (2 marks each)
Section A includes ten brief questions that test theoretical knowledge and quick understanding of AI fundamentals. The topics include:
Definition and role of Artificial Intelligence
Different domains of AI
Importance of data
Challenges in speech recognition systems
Real-life benefits of Natural Language Processing (NLP)
Limitations of machine translation
Difference between deep learning and machine learning
Meaning of learning in artificial neural networks
Difference between speech recognition and voice recognition
Definition of computer vision
These questions check a student's base-level familiarity with AI concepts and terminology.
SECTION B – Analytical & Descriptive Questions (10 marks each)
Students must attempt any three out of five questions. These questions require detailed explanations, comparisons, and structured understanding of AI techniques and systems. Topics include:
How AI systems differ from traditional computing systems
Importance of data visualization and tools used for it
Comparison among regression, classification, and clustering, with suitable examples
Working mechanism of Convolutional Neural Networks (CNNs)
Detailed working of face recognition systems
This section helps students demonstrate their analytical approach, algorithmic knowledge, and practical understanding of AI applications.
SECTION C – Long Descriptive, Skill-Based & Technical Questions (10 marks each)
Each subsection requires students to attempt one question, covering deep conceptual knowledge and real-world AI implementations:
Topics include:
Ethical concerns related to Artificial Intelligence
Skills required to become a successful AI Engineer
Step-by-step working of speech recognition systems
Stages of data processing
Structure and working diagram of a Chatbot and difference from virtual assistants
Explanation of Natural Language Understanding (NLU) and Natural Language Generation (NLG)
Comparison between biological and artificial neural networks, and the Universal Approximation Theorem
Working of Generative Adversarial Networks (GANs)
Difference between Robotics and AI
Usage of Image Recognition and Object Identification in Tesla autonomous cars
These questions test the student's ability to think critically, describe algorithms, understand real-world AI technologies, and connect theory with real applications.
Sample Questions From the Paper (1–2 included)
“Define Artificial Intelligence and discuss its role.”
— A foundational short question evaluating a student’s basic understanding of AI.
“Explain step-by-step working of a speech recognition system.”
— A detailed long-form question assessing technical understanding of speech-based AI systems.
These questions represent the overall variety and depth included in the full paper.
Overall Coverage of This Question Paper
This AI question paper covers the entire syllabus comprehensively, including:
AI fundamentals & applications
Data, information, and data processing
Machine learning types and comparisons
Deep learning and neural networks
Computer vision and speech recognition
Chatbots, virtual assistants, and natural language processing
AI ethics, responsibilities, and real-world impact
GANs and CNNs
Robotics vs. Artificial Intelligence
Autonomous car intelligence (e.g., Tesla AI)
It ensures that students demonstrate understanding of both the theoretical foundation and practical applications of AI.