Artificial Intelligence MCQ (Multiple Choice Questions) - SchoolingAxis

Artificial Intelligence MCQ (Multiple Choice Questions)

 Que- How many types of quantification are available in artificial intelligence? 

a. 1 

b. 2 

c. 3 

d. 4 


Ans- 2  


Que- What kind of interpretation is done by adding context-dependant information? 

a. Semantic 

b. Syntactic 

c. Pragmatic 

d. None of the mentioned   


Ans- Pragmatic  


Que- What enables people to recognize people, animals and inanimate objects reliably? 

a. Speech 

b. Vision 

c. Hear 

d. Perception   


Ans- Vision  


Que- How many types of recognition are there in artificial intelligence? 

a. 1 

b. 2 

c. 3 

d. 4 


Ans- 3  


Que- Which are recognized by vision? 

a. Objects 

b. Activities 

c. Motion 

d. Both Objects & Activities   


Ans- Both Objects & Activities    


Que- Which provides a framework for studying object recognition? 

a. Learning 

b. Unsupervised learning 

c. Supervised learning 

d. None of the mentioned   


Ans- Supervised learning  


Que- Which object recognition process is an error-prone process? 

a. Bottom-up segmentation 

b. Top-down segmentation 

c. Both Bottom-up & Top-down segmentation 

d. None of the mentioned   


Ans- Bottom-up segmentation  


Que- Which is the only way to learn about the different kinds of human faces? 

a. Perception 

b. Speech 

c. Learning 

d. Hearing   


Ans- Learning  


Que- What can be represented by using histograms or empirical frequency distributions? 

a. Words 

b. Color 

c. Texture 

d. Both Color & Texture   


Ans- Both Color & Texture    


Que- Which can be deformed into alignment using simple coordinate transformations? 

a. Matching 

b. Deformable matching 

c. Feature 

d. All of the mentioned   


Ans- Deformable matching  


Que- Which describes the coarse arrangement of the rest of the shape with respect to the point? 

a. Shape 

b. Context 

c. Shape context 

d. None of the mentioned   


Ans- Shape context  


Que- How the distance between two shapes can be defined? 

a. Weighted sum of the shape 

b. Size of the shape 

c. Shape context 

d. None of the mentioned   


Ans- Weighted sum of the shape  


Que- How many issues are available in describing degree of belief? 

a. 1 

b. 2 

c. 3 

d. 4 


Ans- 2  


Que- What is used for probability theory sentences? 

a. Conditional logic 

b. Logic 

c. Extension of propositional logic 

d. None of the mentioned   


Ans- Extension of propositional logic  


Que- Where does the dependance of experience is reflected in prior probability sentences? 

a. Syntactic distinction 

b. Semantic distinction 

c. Both Syntactic & Semantic distinction 

d. None of the mentioned   


Ans- Syntactic distinction  


Que- Where does the degree of belief are applied? 

a. Propositions 

b. Literals 

c. Variables 

d. Statements   


Ans- Propositions  


Que- How many formal languages are used for stating propositions? 

a. 1 

b. 2 

c. 3 

d. 4 


Ans- 2  


Que- What is the basic element for a language? 

a. Literal 

b. Variable 

c. Random variable 

d. All of the mentioned   


Ans- Random variable  


Que- How many types of random variables are available? 

a. 1 

b. 2 

c. 3 

d. 4 


Ans- 3  


Que- Which is the complete specification of the state of the world? 

a. Atomic event 

b. Complex event 

c. Simple event 

d. None of the mentioned   


Ans- Atomic event  


Que- Which variable cannot be written in entire distribution as a table? 

a. Discrete 

b. Continuous 

c. Both Discrete & Continuous 

d. None of the mentioned   


Ans- Continuous  


Que- What is meant by probability density function? 

a. Probability distributions 

b. Continuous variable 

c. Discrete variable 

d. Probability distributions for Continuous variables   


Ans- Probability distributions for Continuous variables    


Que- How many terms are required for building a bayes model? 

a. 1 

b. 2 

c. 3 

d. 4 


Ans- 3  


Que- What is needed to make probabilistic systems feasible in the world? 

a. Reliability 

b. Crucial robustness 

c. Feasibility 

d. None of the mentioned   


Ans- Crucial robustness  


Que- Where does the bayes rule can be used? 

a. Solving queries 

b. Increasing complexity 

c. Decreasing complexity 

d. Answering probabilistic query   


Ans- Answering probabilistic query    


Que- What does the bayesian network provides? 

a. Complete description of the domain 

b. Partial description of the domain 

c. Complete description of the problem 

d. None of the mentioned   


Ans- Complete description of the domain  


Que- How the entries in the full joint probability distribution can be calculated? 

a. Using variables 

b. Using information 

c. Both Using variables & information 

d. None of the mentioned   


Ans- Using information  


Que- How the bayesian network can be used to answer any query? 

a. Full distribution 

b. Joint distribution 

c. Partial distribution 

d. All of the mentioned   


Ans- Joint distribution  


Que- How the compactness of the bayesian network can be described? 

a. Locally structured 

b. Fully structured 

c. Partial structure 

d. All of the mentioned   


Ans- Locally structured  


Que- To which does the local structure is associated? 

a. Hybrid 

b. Dependant 

c. Linear 

d. None of the mentioned   


Ans- Linear  


Que- Which condition is used to influence a variable directly by all the others? 

a. Partially connected 

b. Fully connected 

c. Local connected 

d. None of the mentioned   


Ans- Fully connected  


Que- What is the consequence between a node and its predecessors while creating bayesian network? 

a. Functionally dependent 

b. Dependant 

c. Conditionally independent 

d. Both Conditionally dependant & Dependant   


Ans- Conditionally independent  


Que- Fuzzy logic is a form of 

a. Two-valued logic 

b. Crisp set logic 

c. Many-valued logic 

d. Binary set logic  


Ans- Many-valued logic  


Que- Traditional set theory is also known as Crisp Set theory. 

a. TRUE 

b. False  

c. Nothing can be said 

d. None of the mentioned 


Ans- TRUE  


Que- The truth values of traditional set theory is ____________ and that of fuzzy set is __________ 

a. Either 0 or 1, between 0 & 1 

b. Between 0 & 1, either 0 or 1 

c. Between 0 & 1, between 0 & 1 

d. Either 0 or 1, either 0 or 1  


Ans- Either 0 or 1, between 0 & 1  


Que- Fuzzy logic is extension of Crisp set with an extension of handling the concept of Partial Truth. 

a. TRUE 

b. False  

c. Nothing can be said 

d. None of the mentioned 


Ans- TRUE  


Que- The room temperature is hot. Here the hot (use of linguistic variable is use 

a. can be represented by _______ 

b. Fuzzy Set 

c. Crisp Set 

d. Fuzzy & Crisp Set 


Ans- can be represented by _______  


Que- The values of the set membership is represented by 

a. Discrete Set 

b. Degree of truth 

c. Probabilities 

d. Both Degree of truth & Probabilities  


Ans- Degree of truth  


Que- Japanese were the first to utilize fuzzy logic practically on high-speed trains in Sendai. 

a. TRUE 

b. False  

c. Nothing can be said 

d. None of the mentioned 


Ans- TRUE  


Que- Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following. 

a. AND 

b. OR 

c. NOT 

d. All of the mentioned  


Ans- All of the mentioned   


Que- There are also other operators, more linguistic in nature, called __________ that can be applied to fuzzy set theory. 

a. Hedges 

b. Lingual Variable 

c. Fuzz Variable 

d. None of the mentioned  


Ans- Hedges  


Que- Fuzzy logic is usually represented as 

a. IF-THEN-ELSE rules 

b. IF-THEN rules 

c. Both IF-THEN-ELSE rules & IF-THEN rules 

d. None of the mentioned  


Ans- IF-THEN rules  


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