# 100+ Solved Artificial Intelligence MCQs Questions & Answers

Artificial Intelligence MCQs On AI introduction,  What is AI, Foundations of AI, History of AI, Problem Solving, Searching for Solutions, Types of Search Strategies, Uninformed Search, Breadth-First Search, Depth-First Search, Iterative Deepening, Functions, Local Search Algorithms and Optimization Problems, Adversarial Search: Minmax Algo, Alpha Beta Pruning, Introduction to Reasoning and Knowledge Representation, Propositional Logic, Rules of Inferences, First Order logic.

Syntax and semantics, Inference in first-order logic, Forward and Backward chaining, Resolution, Uncertainty, and Reasoning Bayes’ Theorem, Decision Making, Decision Network, Supervised and Unsupervised, Classification, Clustering, Decision Tree, Introduction to Prolog

### Artificial Intelligence MCQs

1. Who is the father of AI?
A. Karl Peason
B. John Mccarthy
C. Mark
D. James Gosling

2. Artificial Intelligence Introduce In?
A. 1956
B. 1965
C. 1962
D. 1955

3. Machine Learning Is Introduced In?
A. 1959
B. 1956
C. 1969
D. 1965

4. Formula Of A* Algorithm Is.
A. F(N)=G(N) + H(N)
B. F(N)=G(N) – H(N)
C. F(N)=G(N) / H(N)
D. F(N)=G(N) * H(N)

5. 8- Puzzle Problem Without Heuristic Vales Are Also Called?
A. Uninformed Search
B. Informed Search
C. Bubble Sort
D. Binary Search

6. 8- Puzzle Problem With Heuristic Vales Are Also Called?
A. Uninformed Search
B. Informed Search
C. Bubble Sort
D. Binary Search

7. The Science And Engineering Of Making Intelligent Machines Called?
A. Ai
B. Machine Learning
C. Deep Learning
D. Data Mining

8. Python Developed In ?
A. 1989
B. 1998
C. 1997
D. 1979

A. Ai
B. Machine Learning
C. Data Mining
D. Data Where Housing

10. Python Was Created By?
A. Guido Rossum
B. Arthur Samuel
C. John Mccarthy
D. James Gosling

11. Encounter Some Missing Values, Corrupted Data, And Remove Unnecessary Data Called?
A. Data Mining
B. Data Cleaning
C. Redundancy Control
D. Data Where Housing

12. Clustering Is A Problem Of?
A. Regression
B. Classification
C. Un-Supervised Learning
D. Reinforcement

13. Whose Machine Learning Type Is Based On Reward?
A. Supervised Learning
B. Reinforcement
C. A & B Both
D. Un-Supervised Learning

14. Robot’s “Arm” Is Called?
A. Actuator
B. Effector
C. Manipulator
D. Sensors

15. The Conference That Launches The Ai Revolution Was Held In?
A. Harvard
B. Dartmouth
C. New York
D. London

16. What Is Ai ?
A. Playing Games
B. Programming With Your Own Intelligence
C. Making Machines Intelligent
D. Solve Problems

17. Algorithm Is A Supervised Learning Method For Multilayer Perceptron?
A. Back Propagation
B. Classification
C. Clustering
D. Grouping

18. The Process Of Deriving Meaningful Information From A Lot of Data Is Called?
A. Extraction
B. Data Mining
C. Data Where Housing
D. Abstraction

19. NLP Stands For?
A. Neural Language Processing
B. Natural Language Processing
C. Network Language Processing
D. None Linear Problem

20. The Process Of Splitting The Whole Data Into Smaller Chunks Called?
A. Splitting
B. Tokenization
C. Sorting
D. Division

21. Normalize Words Into Its Base Form Or Root Form?
A. Spanning
B. Stemming
C. Sorting
D. Normalization

22. To Overcome The Limitation Of Stemming —— Is Used ?
A. Lemmatization
B. Normalization
C. Erosion
D. Spanning

23. A Set Of Commonly Used Words In Any Language, Not Just English ?
A. Higher Language
B. Stop Words
C. Predefine Words
D. Common Words

24. In Which Conference John Mccarthy First Coined The Term “Artificial Intelligence” In 1956 ?
A. Dartmouth Conference.
B. Acm Conference
C. Aaai Conference
D. Aaai Conference

25. Which Programming Language Is Best Choice For Development Of Ai ?
A. Python
B. Java
C. Dot .Net
D. Mat-Lab

27. There Are How Many Steps In Machine Learning To Solve A Problem ?
A. 3 Steps
B. 5 Steps
C. 7 Steps
D. 9 Steps

28. In Supervised Learning There Is Given ?
A. Both Inputs & Outputs
B. Only Inputs
C. Only Outputs
D. None

29. In Unsupervised Learning There Is Given.
A. Both Inputs & Outputs
B. Only Inputs
C. Only Outputs
D. None

30. In Supervised Learning Type Of Data Is.
A. Labeled Data
B. Unlabeled Data
C. Both
D. None

31. In Unsupervised Learning Type Of Data Is.
A. Labeled Data
B. Unlabeled Data
C. Both
D. None

32. Which Algorithm Is Example Of Supervised Learning ?
A. K-Nearest Neighbors
B. K-Means
C. Both
D. None

33. Which Algorithm Is Example Of Unsupervised Learning ?
A. K-Means
B. K-Nearest Neighbors
C. Both
D. None

34. What Is Date The Birth Of Ai ?
A. 1958
B. 1965
C. 1956
D. 1959

35. What Is The Name Of First Research Lab For Ai ?
A. Allen Institute Of Ai
B. Robotics Laboratory
C. First Al Laboratory
D. Turing Institute

36. In Which Year First Ai Research Lab Developed ?
A. 1956
B. 1959
C. 1965
D. 1960

37. First Robot Was Introduced To General Motors Assembly Line In ?
A. 1960
B. 1959
C. 1965
D. 1956

38. First Robot Was Introduced In 1960 To Which Assembly Line ?
A. Ai Laboratory
B. General Motor’s Assembly Line
C. Allen Institute Of Ai
D. Turing Institute

39. The First Al Chat-Bot Name Which Introduced In 1961 ?
A. Alexa
B. Siri
C. Eliza
D. Cortana

40. Who Won 2005 Darpa Grand Challenge ?
A. Stanford Team
B. Red Team
C. Team Gray
D. Team Terramax

41. When Was Developed Ibm Watson ?
A. 2017
B. 2011
C. 2015
D. 2019

42. What Is The Purpose Of Ibm Watson ?
A. Computer Hardware
B. Making Games
C. Chatbot

43. On Which Platform Alexa Assistant Is Working  ?
A. Pixabay
B. Amazon
D. Yahoo

44. Why We Need Ai Those Days ?
A. Massive Amount Of Data
B. To Develop Machines
C. Decrease Population
D. Increase Population

45. Which Algorithm Is Example Of Reinforcement Learning ?
A. Supervised Learning
B. Q-Learning
C. Unsupervised Learning
D. Semi Supervised Learning

46. Unsupervised Learning Solves Which Type Of Problems ?
A. Clustering
B. Regression
C. Classification
D. None Of Them

47. Supervised Learning Solves Which Type Of Problems ?
A. Clustering
B. Regression
C. Classification
D. None Of Them

48. Reinforcement Learning Solve Which Type Of Problems ?
A. Clustering
B.Reward Based
C. Regression
D. Classification

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49. In Reinforcement Learning Type Of Data Is.
A. Labeled Data
B. Unlabeled Data
C. No Pre-Defined Data
D. Both A & B

50. In Supervised Learning Training Is Under.
A. External Supervision
B. Internal Supervision
C. No Supervision
D. None Of Them

51. In Unsupervised Learning Training Is Under.
A. External Supervision
B. Internal Supervision
C. No Supervision
D. None Of Them

52. In Reinforcement Learning Training Is Under.
A. External Supervision
B. Internal Supervision
C. No Supervision
D. None Of Them

53. In Supervised Learning Output Is.
A. Known
B. Unknown
C. Both A & B
D. None Of Them

54. In Unsupervised Learning Output Is.
A. Known
B. Unknown
C. Both A & B
D. Understand Patterns And Discover Output

55. In Reinforcement Learning Output Is.
A. Understand Patterns And Discover Output
B. Known
C. Follow Trail And Error Method
D. Unknown

56. What Is Example Of Regression ?
A. Predict Stock Market Price
B. Spam And Non-Spam Emails
C. Transactions That Are Fraud In Nature
D. None Of Them

57. What Is Example Of Classification ?
A. Predict Stock Market Price
B. Spam And Non-Spam Emails
C. Transactions That Are Fraud In Nature
D. None Of Them

58. What Is Example Of Clustering ?
A. Predict Stock Market Price
B. Spam And Non-Spam Emails
C. Transactions That Are Fraud In Nature
D. None Of Them

59. Which Is Regression Algorithm.
A. Linear Regression
B. Logistic Regression
C. Both A & B
D. None Of Them

60. Which Is Classification Algorithm.
A. Linear Regression
B. Logistic Regression
C. Both A & B
D. None Of Them

61. Output Of Regression Model Is.
A. Categorical Quantity
B.Continuous Quantity
C. Both A & B
D. None Of Them

62. Output Of Classification Model Is.
A. Categorical Quantity
B.Continuous Quantity
C. Both A & B
D. None Of Them

63. Decision Tree Is Which Type Of Machine Learning Algorithm.
A. Supervised Learning
B. Q-Learning
C. Unsupervised Learning
D. Semi Supervised Learning

64. In Decision Tree Root Node Represent.
A. Input
B. Output
C. Predictor Variable
D. None Of Them

65. In Decision Tree Each Leaf Node Represent.
A. Input
B. Output
C. Predictor Variable
D. None Of Them

66. Multiple Decision Tree Combine To Make A.
A. K-Means
B. Cnn
C. Random Forest
D. None Of Them

67. If You Want To Avoid Over-Fitting You Use.
A. K-Means
B. Random Forest
C. Cnn
D. None Of Them

68. The Sample Dataset That Does Not Include In Bootstrapped Dataset Is Called.
A. Bag Of Words
B. Tfidf
C. Out-Of-Bag (Oob)Dataset
D. None Of Them

69. In Real World, How Much Data Is Not Included In Bootstrap Dataset ?
A. 1/3rd
B. 2/3rd
C. 0/3rd
D. None Of Them

70. Naive Bayes Solve Which Type Of Problems.
A. Classification
B. Regression
C. Both A & B
D. None Of Them

71. The Approach That Is Used By Naive Bayes To Solve A Problem.
A. Realistic Approach
B. Probabilistic Approach
C. Both A & B
D. None Of Them

72. Which Variable Or Which Feature Best Splits The Data.
A. Information Gain
B. Entropy
C. Both A & B
D. None Of Them

73. Why Random Forest ?
A. More Accuracy
B. Avoid Over-Fitting
C. Bagging
D. All Of Them

74. Which Algorithm Is Used To Classify Data Into Different Classes ?
A. Random Forest
B. Knn
C. Svm
D. Linear Regression

75. Box-Plot Is Mainly Used In ?
A. Data Distribution
B. Data Gathering
C. Exploratory Data Analysis
D. None Of Them

76. Which Concept Deep Learning Used To Solve Complex Problems.
A. Svm
B. Neural Networks
C. Knn
D. Cnn

77. Limitation Of Machine Learning is.
A. To Handle High Dimensional Data
B. To Give Accurate Output
C. To Handle Low Dimensional Data
D. None Of Them

78. Deep Learning Is One Of The Only Method By Which We Can Overcome The Challenges Of.
A. Prediction
B. Feature Extraction
C. Training Model
D. None Of Them

79. The First Layer Of Deep Learning Is Known As.
A. Input Layer
B. Output Layer
C. Hidden Layer
D. None Of Them

80. A Single Layer Perceptron is.
A. Linear Classifier
B. Binary Classifier
C. Both A & B
D. None Of Them

81. Multilayer Perceptron Contain One Or More Hidden Layer That’s Why It Considers.
A. Neural Network
B. Deep Neural Network
C. Svm
D. None Of Them

82. A Very Important Concept Of The Multiple Layer Perceptron Is.
A. Feature Extraction
B. Data Processing
C. Back Propagation
D. None Of Them

83. Training A Neural Network Is All About.
A. Inputs
B. Back Propagation
C. Desired Outputs
D. None Of Them

84. Which Method Is Used To Reduced The Error / Loss In The Network.
B. Gbm
C. Svm
D. None Of Them

85. For Scaling Which Method Is Used.
A. Scaling
B. Minmaxscaler
C. Measuring
D. None Of Them

86. Breaking A Complex Sentences Into Words Is Called.
A. Stemming
B. Lemmatization
C. Tokenization
D. None Of Them

87. Normalizing Words Into Its Base Form Is Known As.
A. Stemming
B. Lemmatization
C. Tokenization
D. None Of Them

88. The Morphological Analysis Of Words Is Done By.
A. Stemming
B. Lemmatization
C. Tokenization
D. None Of Them

89. The Intelligence Displayed By Humans And Other Animals Is Termed.
A. Constance
B. Ability
C. Natural Intelligence
D. Cognition

90. An Evolved Definition Of Artificial Intelligence Led To A Phenomenon Known As The
A. Formulation
B. Data Processing
C. Ai Effect
D. Machination

91. The Nodes That Have No Child Is Called____?
B. Root Node
C. Both Of Them
D. None Of These

92. If A Machine Answers Ambiguous Questions, Then It Is Called.
A. Machine
B. Intelligent Machine
C. Local Machine
D. Fast Machine

93. A Systematic Approach H To Solve A Problem Is Called .
A. Problem Space
B. Line Space
C. Tree Space
D. None Of These

94. The Graphical Representation Of Solution Space Can Also Be Done With___?
A. Node
B. Tree
C. Table
D. None Of These

95. The Node Which Has No Parent Is Called___.
A. Leaf Node
B. Decision Node
C. Both A And B
D. Root Node

96. After Finding All The Child Of A Node, Then A Node Is Said To Be ?
A. Open Node
B. Closed Node
C. Child Node
D. Middle Node

97. A Node That Has One Or More Link Between Parent And Itself Is Called____.
A. Decendant
B. Ancestor
C. Root
D. None Of These

98. Blind Search Is Also Known As.
A. Informed Search
B. Heuristic Search
C. Uninformed Search
D. Simple Search

99. Number Of Nodes Expending From A Node To Calculate An Average Is Called.
A. Height Of Tree
B. Depth Of Tree
C. Level Of Tree
D. Branching Factor

100. The Lowest Leaf Of A Tree.
A. Height Of A Tree
B. Length Of A Tree
C. Level Of A Tree
D. None Of A Tree

101. Category Of Information That Control A Search .
A. Dfs
B. Bfs
C. Linear Search
D. Heuristic Search

102. The Search That Stops Instantly After Finding The Solution.
A. Optimal Search
B. Non-Optimal Search
C. Informed Search
D. Depth First Search

103. The Major Problem With Breath First Search Is.
A. Space
B. Time
C. Resources

104. Deeping Progression Emulates Two Techniques.
A. Dfs & Beam Search
B. Bfs & Beam Search
C. Dfs & Bfs