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Welcome to the Uncertain Knowledge MCQs Page

Dive deep into the fascinating world of Uncertain Knowledge with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Uncertain Knowledge, a crucial aspect of Artificial Intelligence. In this section, you will encounter a diverse range of MCQs that cover various aspects of Uncertain Knowledge, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Artificial Intelligence.

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Check out the MCQs below to embark on an enriching journey through Uncertain Knowledge. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Artificial Intelligence.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Uncertain Knowledge. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Uncertain Knowledge MCQs | Page 6 of 6

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Q51.
How many terms are required for building a bayes model?

a.

1

b.

2

c.

3

d.

4

Discuss
Answer: (c).3
Q52.
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

Discuss
Answer: (a).Complete description of the domain
Q53.
____________ 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

Discuss
Answer: (c).Inference
Q54.
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't Say

Discuss
Answer: (a).True
Q55.
Bayesian networks are a factorized representation of the full joint.

a.

True

b.

False

c.

Can be true or false

d.

Can't Say

Discuss
Answer: (a).True
Q56.
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

Discuss
Answer: (c).Conditionally independent
Q57.
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

Discuss
Answer: (b).Fully connected
Q58.
To which does the local structure is associated?

a.

Hybrid

b.

Dependant

c.

Linear

d.

None of the above

Discuss
Answer: (c).Linear
Q59.
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't Say

Discuss
Answer: (a).True
Page 6 of 6

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