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Artificial Intelligence (AI) MCQs - Unit-1
1. What is Artificial Intelligence (AI)?
A . A branch of computer science dealing with hardware
B . A field focused on creating machines that can perform tasks requiring human intelligence
C . A type of software used for graphic design
D . A programming language
2. An intelligent agent is:
A . A robot that can walk
B . A system that perceives its environment and takes actions to achieve goals
C . A type of database
D . A computer virus
3. Problem-solving agents are designed to:
A . Play games only
B . Solve specific problems by searching for solutions
C . Manage computer networks
D . Create animations
4. Which of the following is an uninformed search strategy?
A . Greedy best-first search
B . A* search
C . Breadth-first search
D . Hill-climbing search
5. Breadth-first search (BFS) explores:
A . The deepest node first
B . The shallowest node first
C . Nodes randomly
D . Nodes based on a heuristic
6. Uniform cost search (UCS) is optimal when:
A . All step costs are equal
B . All step costs are non-negative
C . The search space is small
D . The heuristic is admissible
7. Depth-first search (DFS) explores:
A . The shallowest node first
B . The deepest node first
C . Nodes based on a heuristic
D . Nodes randomly
8. Iterative deepening depth-first search (IDDFS) combines:
A . BFS and DFS
B . UCS and A* search
C . Greedy search and hill-climbing
D . Simulated annealing and local search
9. Bidirectional search is efficient when:
A . The search space is small
B . The goal state is known
C . The heuristic is inadmissible
D . The step costs are zero
10. Greedy best-first search uses:
A . A heuristic to estimate the cost to the goal
B . The total path cost from the start node
C . Random exploration
D . Depth-limited search
11. A search is optimal if the heuristic is:*
A . Inadmissible
B . Consistent and admissible
C . Random
D . Not used
12. A heuristic function is:
A . A function that calculates the exact cost to the goal
B . A function that estimates the cost to the goal
C . A function that generates random numbers
D . A function used only in uninformed search
13. Hill-climbing search is a type of:
A . Uninformed search
B . Local search
C . Global search
D . Adversarial search
14. Simulated annealing search is inspired by:
A . Biological evolution
B . The process of heating and cooling metals
C . Random walks
D . Depth-first search
15. Local search in continuous spaces deals with:
A . Discrete states only
B . Continuous variables
C . Only binary variables
D . None of the above
16. Which search strategy is complete but not optimal?
A . Breadth-first search
B . Depth-first search
C . Uniform cost search
D . A* search
17. Which search strategy uses a priority queue based on path cost?
A . Breadth-first search
B . Depth-first search
C . Uniform cost search
D . Greedy best-first search
18. Which search strategy is prone to getting stuck in local maxima?
A . Hill-climbing search
B . A* search
C . Breadth-first search
D . Uniform cost search
19. Which search strategy is guaranteed to find the shortest path in a graph?
A . Depth-first search
B . Breadth-first search
C . Greedy best-first search
D . Hill-climbing search
20. Which search strategy is both complete and optimal?
A . Depth-first search
B . Breadth-first search
C . A* search
D . Greedy best-first search
21. Which of the following is NOT an uninformed search strategy?
A . Breadth-first search
B . Depth-first search
C . Uniform cost search
D . Greedy best-first search
22. Which search strategy uses a stack for node exploration?
A . Breadth-first search
B . Depth-first search
C . Uniform cost search
D . A* search
23. Which search strategy uses a queue for node exploration?
A . Breadth-first search
B . Depth-first search
C . Uniform cost search
D . Greedy best-first search
24. Which search strategy is most likely to get stuck in an infinite loop?
A . Breadth-first search
B . Depth-first search
C . Uniform cost search
D . A* search
25. Which search strategy is best suited for large search spaces with a known goal state?
A . Bidirectional search
B . Depth-first search
C . Hill-climbing search
D . Simulated annealing search
26. Which search strategy is inspired by the natural process of metal cooling?
A . Hill-climbing search
B . Simulated annealing search
C . A* search
D . Greedy best-first search
27. Which search strategy is NOT guaranteed to find a solution?
A . Breadth-first search
B . Depth-first search
C . Hill-climbing search
D . Uniform cost search
28. Which search strategy uses a heuristic to prioritize nodes?
A . Breadth-first search
B . Depth-first search
C . Greedy best-first search
D . Uniform cost search
29. Which search strategy is used to solve optimization problems?
A . Breadth-first search
B . Depth-first search
C . Hill-climbing search
D . Uniform cost search
30. Which search strategy is both complete and optimal for graphs with non-negative edge costs?
A . Breadth-first search
B . Depth-first search
C . A* search
D . Greedy best-first search
Fill in the Blanks
31. Artificial Intelligence (AI) is the field of creating machines that can perform tasks requiring __________ intelligence.
32. An intelligent agent perceives its environment through __________ and takes actions to achieve its goals.
33. Problem-solving agents use __________ to find solutions to specific problems.
34. Breadth-first search (BFS) explores the __________ nodes first.
35. Depth-first search (DFS) explores the __________ nodes first.
36. Uniform cost search (UCS) prioritizes nodes based on __________.
37. Iterative deepening depth-first search (IDDFS) combines the benefits of __________ and __________.
38. Bidirectional search performs two simultaneous searches: one from the start node and one from the __________ node.
39. Greedy best-first search uses a __________ function to estimate the cost to the goal.
40. A search is optimal if the heuristic is __________ and __________.*
41. A heuristic function provides an __________ of the cost to reach the goal.
42. Hill-climbing search is a __________ search algorithm that moves toward the highest-valued neighbor.
43. Simulated annealing search is inspired by the process of __________ and __________ metals.
44. Local search in continuous spaces deals with __________ variables.
45. Breadth-first search is __________ but not always __________.
46. Depth-first search is not guaranteed to find the __________ path.
47. Uniform cost search is optimal for graphs with __________ edge costs.
48. A search uses the formula __________ to evaluate nodes.*
49. Hill-climbing search can get stuck in __________ maxima.
50. Simulated annealing search allows __________ moves to escape local optima.
51. Bidirectional search reduces the __________ of the search space.
52. Greedy best-first search is not __________ because it does not consider the total path cost.
53. A heuristic function is __________ if it never overestimates the cost to the goal.
54. Depth-first search uses a __________ data structure for node exploration.
55. Breadth-first search uses a __________ data structure for node exploration.
56. Iterative deepening depth-first search is a combination of __________ and __________.
57. Local search algorithms are useful for solving __________ problems.
58. Simulated annealing search is a __________ search algorithm.
59. A search is both __________ and __________ if the heuristic is admissible and consistent.*
60. In continuous spaces, local search algorithms deal with __________ variables instead of discrete states.
Relevant Materials :
☞ Artificial Intelligence (AI) MCQs - Unit-1 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-2 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-3 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-4 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-5 - [ Artificial Intelligence ]
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