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