SECTION A – Very Short Answer Questions
(2 × 7 = 14 Marks | Attempt all)
Purpose of Section A
Section A checks your basic understanding, definitions, and core concepts of Artificial Intelligence. This section is meant to test whether you know the fundamental terminology of AI.
Nature of Questions
Questions are: Definition based
Difference based Listing based
From the paper, Section A includes: Heuristic function
Supervised vs unsupervised learning Characteristics of an intelligent agent
Uniform search techniques Forward vs backward chaining
Bayes’ rule Reinforcement learning
How to Write Answers in Section A
Write 2–4 lines only Be direct and precise
No examples unless very short No diagrams required
Example (Expected Level)
Q: What is a heuristic function?
A heuristic function is a function that estimates the cost from the current state to the goal state and is used to guide informed search algorithms.
This section is easy scoring if definitions are correct.
SECTION B – Medium Answer Questions
(7 × 3 = 21 Marks | Attempt any three)
Purpose of Section B
Section B checks your conceptual clarity and explanation ability. Here, the examiner wants to see whether you understand how AI techniques work, not just their definitions.
Nature of Questions
Questions involve: Algorithms (DFS)
Logical representation (WFF, rules of inference) Learning models (Statistical learning)
Dimensionality reduction (PCA vs LDA) Problem solving approach (State space)
Depth Expected
Each answer should include: Proper definition
Explanation step by step Diagram or example where applicable
How to Write Answers Start with a definition
Explain the working or concept Add a small example or comparison
Length: 1.5 to 2 pages
Example Expectation For DFS algorithm:
Define DFS Explain stack-based traversal
Give a small graph example Mention advantages and limitation
This section needs clarity + structure.
SECTION C – Long Answer / Analytical Questions
(7 × 5 = 35 Marks | One question from each unit)
Purpose of Section C
Section C is the most important and highest-scoring section. It tests:
In-depth understanding Analytical thinking
Presentation skills
Nature of Questions
Questions are descriptive and application-oriented, covering almost all AI units:
From the paper, topics include: Classification of AI
Natural Language Understanding (syntax & semantics) Alpha-Beta pruning (with game tree)
Constraint Satisfaction Problems (Cryptarithmetic) Resolution & Unification
Decision Trees & EM Algorithm Pattern Recognition system
Support Vector Machine (SVM)
Depth Expected
Each answer should include: Clear definition
Theory explanation Algorithm or steps
Diagrams / trees / block diagrams
Examples
How to Write Answers Use proper headings
Draw neat diagrams Write algorithms step-wise
Length: 2–3 pages per answer
Example Expectation For Alpha-Beta Pruning:
Define Minimax & Alpha-Beta pruning Explain pruning logic
Solve the given game tree Show pruned branches
This section decides top grades.
