CS6400 - Exam Study Questions Solved 100%
Correct | Verified Answers
Reflexive Inference Rule - ✔✔If Y is a subset of X, then X -> Y. i.e. EG -> G
Augmentation Inference Rule - ✔✔If X -> Y, then XZ -> YZ for any Z i.e. E->A, therefore
EG->AG
Transitive Inference Rule - ✔✔If X -> Y and Y -> Z, then X -> Z, i.e. B->E, E->A, therefore B->A
Decomposition Inference Rule - ✔✔If X -> YZ then X -> Y and X -> Z
Composition Inference Rule - ✔✔If X -> Y and A -> B then XA->YB
Union Inference Rule - ✔✔If X -> Y and X-> Z then X -> YZ
Pseudo Transitivity Inference Rule - ✔✔If X -> Y and YZ -> W then XZ -> W
1NF - ✔✔If and only if the domain of each attribute contains only atomic (indivisible)
values, and the value of each attribute contains only a single value from that domain. May
have redundancy.
2NF - ✔✔The table is in 1NF and all the columns depend on the tables' primary
key (transitively or not)
3NF - ✔✔The table is in 2NF and all of its columns not transitively dependent on the
primary key
, Lossless Join Decomposition - ✔✔A concept in which the following are true: Union of
Attributes of R1 and R2 must be equal to attribute of R, the Intersection of Attributes of R1
and R2 must not be NULL, and the Common attribute must be a key for at least one relation
Relational Tuple Calculus (with some Algebra operators) - ✔✔What SQL is
mathematically based upon
NFNF (NF^2) - ✔✔A data structure that is not a relation at all
First Rule of Normalization - ✔✔No redundancy of facts
Second Rule of Normalization - ✔✔No cluttering of facts
Third Rule of Normalization - ✔✔Must preserve information
Fourth Rule of Normalization - ✔✔Must preserve functional dependencies
Information Loss - ✔✔When, after decomposition, we get more (or less) information
than was initially present in the base dataset.
Dependency Loss - ✔✔When we cannot enforce the functional dependencies
Functional Dependence - ✔✔A relationship that exists when one attribute
uniquely determines another attribute.
BCNF - ✔✔A normal form in which every determinant is a candidate key
Correct | Verified Answers
Reflexive Inference Rule - ✔✔If Y is a subset of X, then X -> Y. i.e. EG -> G
Augmentation Inference Rule - ✔✔If X -> Y, then XZ -> YZ for any Z i.e. E->A, therefore
EG->AG
Transitive Inference Rule - ✔✔If X -> Y and Y -> Z, then X -> Z, i.e. B->E, E->A, therefore B->A
Decomposition Inference Rule - ✔✔If X -> YZ then X -> Y and X -> Z
Composition Inference Rule - ✔✔If X -> Y and A -> B then XA->YB
Union Inference Rule - ✔✔If X -> Y and X-> Z then X -> YZ
Pseudo Transitivity Inference Rule - ✔✔If X -> Y and YZ -> W then XZ -> W
1NF - ✔✔If and only if the domain of each attribute contains only atomic (indivisible)
values, and the value of each attribute contains only a single value from that domain. May
have redundancy.
2NF - ✔✔The table is in 1NF and all the columns depend on the tables' primary
key (transitively or not)
3NF - ✔✔The table is in 2NF and all of its columns not transitively dependent on the
primary key
, Lossless Join Decomposition - ✔✔A concept in which the following are true: Union of
Attributes of R1 and R2 must be equal to attribute of R, the Intersection of Attributes of R1
and R2 must not be NULL, and the Common attribute must be a key for at least one relation
Relational Tuple Calculus (with some Algebra operators) - ✔✔What SQL is
mathematically based upon
NFNF (NF^2) - ✔✔A data structure that is not a relation at all
First Rule of Normalization - ✔✔No redundancy of facts
Second Rule of Normalization - ✔✔No cluttering of facts
Third Rule of Normalization - ✔✔Must preserve information
Fourth Rule of Normalization - ✔✔Must preserve functional dependencies
Information Loss - ✔✔When, after decomposition, we get more (or less) information
than was initially present in the base dataset.
Dependency Loss - ✔✔When we cannot enforce the functional dependencies
Functional Dependence - ✔✔A relationship that exists when one attribute
uniquely determines another attribute.
BCNF - ✔✔A normal form in which every determinant is a candidate key
