1. Traditional set theory is also known as Crisp Set theory.
a) True
b) False

a) True
Explanation:
Traditional set theory set membership is fixed or exact either the member is in the set or not. There is only two crisp values true or false. In case of fuzzy logic there are many values. With weight say x the member is in the set.

2. What is the consequence between a node and its predecessors while creating Bayesian network?
a) Conditionally dependent
b) Dependent
c) Conditionally independent
d) Both a & b

c) Conditionally independent
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.

5. Like relational databases there does exists fuzzy relational databases.
a) True
b) False

a) True
Explanation: Once fuzzy relations are defined, it is possible to develop fuzzy relational databases. The first fuzzy relational database, FRDB, appeared in Maria Zemankova's dissertation.

6. _____________ is/are the way/s to represent uncertainty.
a) Fuzzy Logic
b) Probability
c) Entropy
d) All of the mentioned

d) All of the mentioned
Explanation: Entropy is amount of uncertainty involved in data. Represented by H(data).

8. Fuzzy logic is usually represented as
a) IF-THEN-ELSE rules
b) IF-THEN rules
c) Both a & b
d) None of the mentioned

b) IF-THEN rules
Explanation: Fuzzy set theory defines fuzzy operators on fuzzy sets. The problem in applying this is that the appropriate fuzzy operator may not be known. For this reason, fuzzy logic usually uses IF-THEN rules, or constructs that are equivalent, such as fuzzy associative matrices.
Rules are usually expressed in the form:
IF variable IS property THEN action

10. Where does the Bayes rule can be used?
a) Solving queries
b) Increasing complexity
c) Decreasing complexity