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# Artificial Intelligence MCQ Questions - Bayesian Networks

Bayesian Networks MCQs : This section focuses on "Bayesian Networks" in Artificial Intelligence. These Multiple Choice Questions (MCQ) should be practiced to improve the AI skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations.

1. Bayesian Belief Network is also known as ?

A. belief network
B. decision network
C. Bayesian model
D. All of the above

2. Bayesian Network consist of ?

A. 2
B. 3
C. 4
D. 5

3. The generalized form of Bayesian network that represents and solve decision problems under uncertain knowledge is known as an?

A. Directed Acyclic Graph
B. Table of conditional probabilities
C. Influence diagram
D. None of the above

4. How many component does Bayesian network have?

A. 2
B. 3
C. 4
D. 5

5. The Bayesian network graph does not contain any cyclic graph. Hence, it is known as a

A. DCG
B. DAG
C. CAG
D. SAG

6. In a Bayesian network variable is?

A. continuous
B. discrete
C. Both A and B
D. None of the above

7. If we have variables x1, x2, x3,....., xn, then the probabilities of a different combination of x1, x2, x3.. xn, are known as?

A. Table of conditional probabilities
B. Causal Component
C. Actual numbers
D. Joint probability distribution

8. The nodes and links form the structure of the Bayesian network, and we call this the ?

A. structural specification
B. multi-variable nodes
C. Conditional Linear Gaussian distributions
D. None of the above

9. Which of the following are used for modeling times series and sequences?

A. Decision graphs
B. Dynamic Bayesian networks
C. Value of information
D. Parameter tuning

10. How many terms are required for building a bayes model?

A. 1
B. 2
C. 3
D. 4

11. What is needed to make probabilistic systems feasible in the world?

A. Reliability
B. Crucial robustness
C. Feasibility
D. None of the above

12. Where does the bayes rule can be used?

A. Solving queries
B. Increasing complexity
C. Decreasing complexity

13. 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 above

14. ____________ is the process of calculating a probability distribution of interest e.g. P(A | B=True), or P(A,B|C, D=True).

A. Diagnostics
B. Supervised anomaly detection
C. Inference
D. Prediction

15. The Distributive law simply means that if we want to marginalize out the variable A we can perform the calculations on the subset of distributions that contain A.

A. TRUE
B. FALSE
C. Can be true or false
D. Can not say

16. Bayesian networks are a factorized representation of the full joint.

A. TRUE
B. FALSE
C. Can be true or false
D. Can not say

17. 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

18. 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 above

19. To which does the local structure is associated?

A. Hybrid
B. Dependant
C. Linear
D. None of the above

20. When we query a node in a Bayesian network, the result is often referred to as the marginal.

A. TRUE
B. FALSE
C. Can be true or false
D. Can not say