(SEM II) THEORY EXAMINATION 2021-22 AI FOR ENGINEERING

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.