APSET 2026 – Artificial Intelligence (AI) MCQs (50 Questions)

(Computer Science & Applications – As per APSET / UGC NET Syllabus)

2. The father of AI is:
A) Alan Turing
B) John McCarthy
C) Marvin Minsky
D) Herbert Simon
👉 Answer: B

Below are 50 important MCQs for APSET 2026 preparation.

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🔹 PART A: Basics of Artificial Intelligence

1. Artificial Intelligence was formally introduced in:
A) 1943
B) 1956
C) 1965
D) 1972
👉 Answer: B

2. The father of AI is:
A) Alan Turing
B) John McCarthy
C) Marvin Minsky
D) Herbert Simon
👉 Answer: B

3. The Turing Test was proposed by:
A) John McCarthy
B) Alan Turing
C) Newell
D) Simon
👉 Answer: B

4. AI primarily deals with:
A) Numerical computation
B) Intelligent agents
C) Hardware design
D) Databases
👉 Answer: B

5. Rational agent acts to:
A) Maximize performance measure
B) Minimize input
C) Reduce memory
D) Increase speed
👉 Answer: A

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🔹 PART B: Search Techniques

6. Breadth First Search uses:
A) Stack
B) Queue
C) Heap
D) Tree
👉 Answer: B

7. Depth First Search uses:
A) Queue
B) Stack
C) Graph
D) Heap
👉 Answer: B

8. BFS guarantees optimal solution when:
A) Path cost is uniform
B) Graph is cyclic
C) Heuristic used
D) No goal state
👉 Answer: A

9. A algorithm uses:*
A) g(n)
B) h(n)
C) g(n) + h(n)
D) h(n) - g(n)
👉 Answer: C

10. Hill Climbing is:
A) Uninformed search
B) Local search
C) Optimal search
D) Adversarial search
👉 Answer: B

11. Which search may get stuck in local maxima?
A) BFS
B) DFS
C) Hill Climbing
D) Uniform Cost
👉 Answer: C

12. Uniform Cost Search expands node with:
A) Lowest heuristic
B) Lowest path cost
C) Highest cost
D) Random choice
👉 Answer: B

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🔹 PART C: Knowledge Representation

13. Knowledge in AI is represented using:
A) Rules
B) Logic
C) Semantic Networks
D) All of the above
👉 Answer: D

14. Propositional logic deals with:
A) Objects
B) Predicates
C) Truth values
D) Functions
👉 Answer: C

15. First Order Logic uses:
A) Quantifiers
B) Variables
C) Predicates
D) All of the above
👉 Answer: D

16. Resolution is used in:
A) Inference
B) Search
C) Learning
D) Parsing
👉 Answer: A

17. Forward chaining is:
A) Data-driven
B) Goal-driven
C) Heuristic
D) Random
👉 Answer: A

18. Backward chaining is:
A) Data-driven
B) Goal-driven
C) Blind search
D) None
👉 Answer: B

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🔹 PART D: Machine Learning

19. Supervised learning uses:
A) Labeled data
B) Unlabeled data
C) No data
D) Random data
👉 Answer: A

20. Unsupervised learning finds:
A) Regression
B) Clusters
C) Labels
D) Output classes
👉 Answer: B

21. Example of supervised algorithm:
A) K-means
B) Decision Tree
C) PCA
D) Apriori
👉 Answer: B

22. K-means is used for:
A) Classification
B) Clustering
C) Regression
D) NLP
👉 Answer: B

23. Overfitting occurs when model:
A) Fits training data too well
B) Underfits
C) Has low variance
D) No noise
👉 Answer: A

24. Bias-Variance tradeoff deals with:
A) Accuracy
B) Error decomposition
C) Data size
D) Speed
👉 Answer: B

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🔹 PART E: Neural Networks

25. Perceptron is used for:
A) Linear classification
B) Clustering
C) Sorting
D) Searching
👉 Answer: A

26. Activation function introduces:
A) Linearity
B) Non-linearity
C) Sorting
D) Memory
👉 Answer: B

27. Backpropagation is used in:
A) Decision Trees
B) Neural Networks
C) Clustering
D) Graph search
👉 Answer: B

28. Sigmoid function output range is:
A) (-∞, ∞)
B) (0,1)
C) (-1,1)
D) (1,∞)
👉 Answer: B

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🔹 PART F: Natural Language Processing (NLP)

29. NLP deals with:
A) Images
B) Audio
C) Human language
D) Numbers
👉 Answer: C

30. Tokenization is:
A) Parsing
B) Splitting text
C) Translation
D) Encryption
👉 Answer: B

31. Part-of-Speech tagging identifies:
A) Syntax
B) Grammar
C) Word category
D) Meaning
👉 Answer: C

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🔹 PART G: Expert Systems

32. Expert systems consist of:
A) Knowledge base
B) Inference engine
C) User interface
D) All of the above
👉 Answer: D

33. MYCIN was an example of:
A) Search engine
B) Expert system
C) DBMS
D) OS
👉 Answer: B

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🔹 PART H: Fuzzy Logic

34. Fuzzy logic deals with:
A) Binary values
B) Crisp sets
C) Degrees of truth
D) Exact truth
👉 Answer: C

35. Membership function defines:
A) Crisp boundary
B) Degree of membership
C) Database
D) Graph
👉 Answer: B

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🔹 PART I: Adversarial Search

36. Minimax algorithm is used in:
A) Sorting
B) Game playing
C) Parsing
D) Learning
👉 Answer: B

37. Alpha-Beta pruning improves:
A) Space complexity
B) Optimality
C) Speed of Minimax
D) Memory
👉 Answer: C

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🔹 PART J: Miscellaneous AI Concepts

38. Heuristic function estimates:
A) Exact cost
B) Optimal path
C) Cost to goal
D) Path length
👉 Answer: C

39. AI planning involves:
A) Searching actions
B) Clustering
C) Sorting
D) Compilation
👉 Answer: A

40. Reinforcement learning is based on:
A) Reward system
B) Labels
C) Clustering
D) Rules
👉 Answer: A

41. Markov Decision Process involves:
A) States
B) Actions
C) Rewards
D) All of the above
👉 Answer: D

42. Bayesian networks are based on:
A) Probability
B) Sorting
C) Search
D) Trees only
👉 Answer: A

43. Genetic algorithms are inspired by:
A) Physics
B) Chemistry
C) Natural evolution
D) Graph theory
👉 Answer: C

44. Mutation in GA introduces:
A) Diversity
B) Accuracy
C) Sorting
D) Speed
👉 Answer: A

45. Crossover in GA combines:
A) Data
B) Parents
C) Rules
D) Labels
👉 Answer: B

46. Constraint Satisfaction Problems involve:
A) Variables & constraints
B) Sorting
C) Parsing
D) Compilation
👉 Answer: A

47. Knowledge engineering is related to:
A) Hardware
B) Expert systems
C) Networks
D) OS
👉 Answer: B

48. Semantic networks represent knowledge using:
A) Tables
B) Graphs
C) Arrays
D) Trees only
👉 Answer: B

49. Frame-based systems organize knowledge into:
A) Rules
B) Objects with slots
C) Trees
D) Numbers
👉 Answer: B

50. AI system that improves from experience exhibits:
A) Learning
B) Searching
C) Sorting
D) Parsing
👉 Answer: A

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✅ Preparation Tips for APSET AI

✔ Focus on search algorithms & ML basics
✔ Practice reasoning & logic questions
✔ Revise neural network concepts
✔ Solve previous APSET & UGC NET papers