Artificial Intelligence Questions & Answers – Bayesian Networks

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This set of Artificial Intelligence Multiple Choice Questions & Answers (MCQs) focuses on “Bayesian Networks”.

1. How many terms are required for building a bayes model?
a) 1
b) 2
c) 3
d) 4
View Answer

Answer: c
Explanation: The three required terms are a conditional probability and two unconditional probability.
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2. What is needed to make probabilistic systems feasible in the world?
a) Reliability
b) Crucial robustness
c) Feasibility
d) None of the mentioned
View Answer

Answer: b
Explanation: On a model-based knowledge provides the crucial robustness needed to make probabilistic system feasible in the real world.

3. Where does the bayes rule can be used?
a) Solving queries
b) Increasing complexity
c) Decreasing complexity
d) Answering probabilistic query
View Answer

Answer: d
Explanation: Bayes rule can be used to answer the probabilistic queries conditioned on one piece of evidence.
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4. 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
View Answer

Answer: a
Explanation: A Bayesian network provides a complete description of the domain.

5. 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
View Answer

Answer: b
Explanation: Every entry in the full joint probability distribution can be calculated from the information in the network.
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6. 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
View Answer

Answer: b
Explanation: If a bayesian network is a representation of the joint distribution, then it can solve any query, by summing all the relevant joint entries.

7. How the compactness of the bayesian network can be described?
a) Locally structured
b) Fully structured
c) Partial structure
d) All of the mentioned
View Answer

Answer: a
Explanation: The compactness of the bayesian network is an example of a very general property of a locally structured system.
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8. To which does the local structure is associated?
a) Hybrid
b) Dependant
c) Linear
d) None of the mentioned
View Answer

Answer: c
Explanation: Local structure is usually associated with linear rather than exponential growth in complexity.

9. 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
View Answer

Answer: b
Explanation: None.
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10. 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
View Answer

Answer: c
Explanation: The semantics to derive a method for constructing bayesian networks were led to the consequence that a node can be conditionally independent of its predecessors.

Sanfoundry Global Education & Learning Series – Artificial Intelligence.

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Manish Bhojasia - Founder & CTO at Sanfoundry
Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He lives in Bangalore, and focuses on development of Linux Kernel, SAN Technologies, Advanced C, Data Structures & Alogrithms. Stay connected with him at LinkedIn.

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