Understanding Database Normalization

What is the primary purpose of normalization in databases?

• To increase data duplication for faster access.

• To simplify database queries by merging tables.

• To enhance the visual representation of data in reports.

• The primary purpose of normalization in databases is to reduce data redundancy and improve data integrity.

Define functional dependency and provide an example.

• In a database, if StudentID -> StudentName, then StudentID functionally determines StudentName.

• StudentName -> StudentID is a functional dependency.

• CourseID -> CourseName does not imply a functional dependency.

• StudentID and StudentName are unrelated attributes.

What are inference rules in the context of functional dependencies?

• Inference rules are methods for optimizing database queries.

• Inference rules are only applicable in programming languages.

• Inference rules are logical rules used to derive new functional dependencies from existing ones.

• Inference rules are used to create new databases from scratch.

How do you compute the closure of a set of functional dependencies?

• The closure is the original set of functional dependencies without any changes.

• The closure is found by removing all attributes from the set until only one remains.

• The closure of a set of functional dependencies is computed by iteratively adding attributes that can be derived from the initial set until no new attributes can be added.

• The closure is computed by randomly selecting attributes from the set.

What is a minimal cover and why is it important?

• A minimal cover is a complete set of functional dependencies that includes all possible dependencies.

• A minimal cover is a set of attributes that can be used to derive all functional dependencies without any simplification.

• A minimal cover is a simplified set of functional dependencies that preserves the original dependencies' closure.

• A minimal cover is a collection of unrelated attributes that do not affect the closure of functional dependencies.

Explain the properties of decomposition in database design.

• Decomposition eliminates all data dependencies.

• The properties of decomposition in database design include lossless join, dependency preservation, redundancy minimization, and improved data integrity.

• Decomposition increases data redundancy.

• Decomposition has no effect on data integrity.

What are the requirements for a relation to be in 1NF?

• A relation is in 1NF if it allows duplicate records.

• A relation is in 1NF if it requires a specific order for records.

• A relation is in 1NF if it has multi-valued attributes.

• A relation is in 1NF if it has atomic values, single type per attribute, unique records, and order independence.

Describe the conditions for a relation to be in 2NF.

• A relation is in 2NF if it has no duplicate rows.

• A relation is in 2NF if all attributes are dependent on each other.

• A relation is in 2NF if it is in 1NF and all non-prime attributes are fully functionally dependent on the primary key.

• A relation is in 2NF if it contains at least one primary key.

What distinguishes 3NF from BCNF?

• BCNF and 3NF are identical in terms of normalization rules

• 3NF requires all attributes to be part of a candidate key

• BCNF is stricter than 3NF; every determinant must be a candidate key in BCNF.

• BCNF allows non-candidate key determinants

What is a multi-valued dependency and how does it relate to 4NF?

• A multi-valued dependency is when one attribute is dependent on another attribute.

• 4NF is achieved by ensuring all attributes are single-valued.

• A multi-valued dependency is a type of dependency where one attribute determines multiple values of another attribute, and it relates to 4NF as a condition that must be eliminated for a relation to be in 4NF.

• A multi-valued dependency is irrelevant to database normalization.