AKTU · MCA · Important questions
AKTU MCA Artificial Intelligence Important Questions
The questions AKTU asks again and again in Artificial Intelligence, found by comparing every question in 5 previous year papers from 2021-22 to 2025-26 (subject codes BMC021, KCA301).
How this list is made: we compared all 122 questions in these papers and grouped the ones that ask about the same thing. Each item shows how many papers it appeared in, which years, and other ways it was worded. Topics that come back often are a strong hint, not a guarantee, so study your full syllabus too.
Most repeated long questions
From Sections B and C (7 or 10 marks, depending on the year). Unit numbers come from Section C, where each question covers one unit.
- Explain alpha-Beta pruning with example
- Describe alpha-beta pruning and give the other modifications to Min-Max
- Describe Alpha-Beta pruning in adversarial search and discuss its advantages.
- Design principles of pattern recognition system. Explain Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
- Explain the concept of Principal Component Analysis (PCA) and evaluate its importance in pattern recognition.
- Discuss k-means clustering algorithm with an suitable example.
- Define the term clustering. Discuss k-means clustering algorithm.
- Explain the working of the k-means clustering algorithm with an example.
- Write short note on the following: i) Reinforcement learning ii) Machine learning.
- Write short note on the following: i) Reinforcement learning ii) Unsupervised learning.
- What is machine learning? Differentiate between supervised, unsupervised and reinforcement learning.
- Convert the following sentence into predicate logic and then prove "Was 1. Marcus was a man. 2. Marcus was a Pompeian. 3. All Pompeian’s were Romans. 4. Caesar was a ruler. 5. All Romans were either loyal to Caesar or hated him. 6. Everyone is loyal to someone. 7. People only try to assassinate rulers they are not loyal to. 8. Marcus tried to assassinate Caesar.
- Describe intelligent agents in terms of Precepts, Actions, Goals and Environment with suitable block diagram and example.
- Describe the use of Hidden Markov models in speech recognition.
- Describe the use of Hidden Markov models and explain it with suitable example
- Discuss and compare forward and backward chaining methods with example.
- Identify the difference between forward and backward chaining? Given the knowledge base as: P, P Q, Q R. Infer R by using forward and backward chaining?
- Explain various steps involved in natural language processing.
Most repeated short questions (2 marks)
From Section A. Learn a crisp definition plus one example or line of explanation for each.
- Compare uninformed and informed search methods.
- What is a search strategy in AI? Give one example each of an uninformed and an informed search technique.
- What are statistical learning models.
- What is Statistical learning method?
- Define the term machine learning.
- Differentiate between supervised learning and unsupervised learning in machine learning.
- Explain naïve bayes classifier.
- State any two foundation areas of artificial intelligence.
- What are the different branches of artificial intelligence?
- What do you mean by the knowledge representation?
- What is an intelligent agent?
- Explain the concept of an intelligent agent with an example.
- Write short note on Support Vector Machine (SVM)
Unit-wise questions from recent papers
Section C questions from the latest papers (2025-26 BMC021, 2024-25 KCA301, 2023-24 KCA301), sorted by unit. Use these to practise one unit at a time.
Unit 1
- What is the Turing Test? Explain its significance in evaluating machine intelligence. (2025-26, BMC021)
- Explain the concept of an intelligent agent and describe the architecture of a basic agent. (2025-26, BMC021)
- Describe intelligent agents in terms of Precepts, Actions, Goals and Environment with suitable block diagram and example. (2024-25, KCA301)
- How can computer vision be used in autonomous vehicles? Explain the role of object detection and depth estimation. (2024-25, KCA301)
- Explain various approaches in NLP? (2023-24, KCA301)
- Differentiate between human intelligence and machine intelligence. (2023-24, KCA301)
Unit 2
- Explain Uniform-Cost Search and illustrate its working with an example. (2025-26, BMC021)
- Describe Alpha-Beta pruning in adversarial search and discuss its advantages. (2025-26, BMC021)
- Give the following graph, apply A* algorithm and find the shortest path from node (a) to node (z). (2024-25, KCA301)
- Examine how AO* doesn’t explore all the solution paths once it got the solution with a suitable example. (2024-25, KCA301)
- Discuss the problem of water jug with heuristic search techniques. (2023-24, KCA301)
- Explain Alpha-Beta pruning with example. (2023-24, KCA301)
Unit 3
- Explain forward chaining and backward chaining in rule-based reasoning, giving one example of each. (2025-26, BMC021)
- Explain resolution in propositional logic with an example. (2025-26, BMC021)
- Write down the algorithm for converting FOPL into CNF and converting Demonstrate the steps on given sentences i. John like all kinds of food. ii. Apples are food. (2024-25, KCA301)
- Convert the following sentence into predicate logic and then prove "Was 1. Marcus was a man. 2. Marcus was a Pompeian. 3. All Pompeian’s were Romans. 4. Caesar was a ruler. 5. All Romans were either loyal to Caesar or hated him. 6. Everyone is loyal to someone. 7. People only try to assassinate rulers they are not loyal to. 8. Marcus tried to assassinate Caesar. (2024-25, KCA301)
- What do you mean by knowledge representation? Describe the techniques of knowledge representation. (2023-24, KCA301)
- Explain the methods of planning & acting in the real world. (2023-24, KCA301)
Unit 4
- Explain the concept of reinforcement learning and give an example of its application. (2025-26, BMC021)
- Describe reinforcement learning and provide a real-life example. (2025-26, BMC021)
- Examine ID3 algorithm to understand the concept of learning using decision tree with proper example. (2024-25, KCA301)
- Describe the two main steps of the EM algorithm. (a) Expectation Step (E-step) (b) Maximization Step (M-step) (2024-25, KCA301)
- Write short note on the following: i) Reinforcement learning ii) Machine learning. (2023-24, KCA301)
- Explain discrete model, Naive Bayes Model & Continuous Model. (2023-24, KCA301)
Unit 5
- Explain the principle of the Support Vector Machine (SVM) for classification. (2025-26, BMC021)
- Explain the concept of Principal Component Analysis (PCA) and evaluate its importance in pattern recognition. (2025-26, BMC021)
- Discuss k-means clustering algorithm with an suitable example. (2024-25, KCA301)
- You are given the following dataset of 5 points with two features (X1, X2), and a class label (A or B): X1 X2 Class 2 3 A 3 3 A 6 5 B 7 8 B 4 4 A Now, consider a new point (X1 = 5, X2 = 6). Apply KNN with K=3 to classify this point. (2024-25, KCA301)
- Define Principle Component Analysis (PCA). Write steps involved in making principle components to do a classification of given data. (2023-24, KCA301)
- Explain Nearest Neighbor (NN) rule in detail. (2023-24, KCA301)
Papers used for this list
- AKTU Artificial Intelligence (BMC021) question paper 2025-26
- AKTU Artificial Intelligence (KCA301) question paper 2024-25
- AKTU Artificial Intelligence (KCA301) question paper 2023-24
- AKTU Artificial Intelligence (KCA301) question paper 2022-23
- AKTU Artificial Intelligence (KCA301) question paper 2021-22
How to use these questions
- Start with the questions asked in the most papers, and make short notes for each.
- Write 2–3 long answers by hand with a timer (about 12–15 minutes each), then compare them with your textbook.
- Revise all the 2-mark questions the night before the exam.
- Finish with one full paper from the list above, timed.
Full plan with AI prompts: How to prepare for AKTU exams using previous year papers. After results, check your marks with the AKTU CGPA calculator.
Frequently asked questions
Are these AKTU Artificial Intelligence questions guaranteed to come in the exam?
No. They are the questions that repeated most in past papers, which makes them a strong hint, but AKTU can ask anything from the syllabus. Use this list to prioritise, not to skip units.
How was this important questions list made?
We compared all 122 questions from 5 AKTU Artificial Intelligence papers (2021-22 to 2025-26) and grouped questions that ask about the same topic, then counted how many papers each appeared in.
Where can I download AKTU Artificial Intelligence previous year papers?
Every paper used here is on LearnAIFast with all questions as text and a link to the original PDF. See the list of papers above.
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