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

 

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

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

Andhra Pradesh State Eligibility Test (APSET) AI unit covers search techniques, knowledge representation, machine learning, reasoning, expert systems, NLP, and neural networks.

Below are 50 important MCQs for APSET 2026 preparation.

🔹 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

🔹 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

🔹 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

🔹 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

🔹 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

🔹 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

🔹 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

🔹 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

🔹 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

🔹 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

✅ 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
✔ Attempt mock tests weekly

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