**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.

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.

c) Fuzzy Set

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

**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.

d) All of the mentioned

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

a) Complete description of the domain

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

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

d) Answering probabilistic query

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