Database Schema Design Quiz

A primary key serves as a unique identifier for each row in a database table, ensuring that no two records are identical. This uniqueness is essential for maintaining data integrity and allows for efficient data retrieval, updates, and relationships between tables.

Second Normal Form (2NF) is designed to eliminate partial dependencies, which occur when a non-key attribute is dependent on only a part of a composite primary key. By ensuring that all non-key attributes are fully functionally dependent on the entire primary key, 2NF enhances data integrity and reduces redundancy in the database schema.

A foreign key is a field in one table that uniquely identifies a row in another table, establishing a link between the two. This relationship enforces referential integrity, ensuring that data remains consistent across both tables. Thus, the statement is true, as foreign keys are essential for relational database structure.

Database normalization aims to organize data efficiently by minimizing redundancy, which helps prevent data anomalies and inconsistencies. By structuring data into related tables and establishing clear relationships, normalization enhances data integrity, ensuring that the information remains accurate and reliable throughout the database.

A good database schema aims to minimize data redundancy to ensure efficiency and consistency. Maximum data duplication contradicts this principle, as it can lead to inconsistencies, increased storage costs, and complications in data management. Therefore, it is not a characteristic of an effective database schema.

A table is in First Normal Form (1NF) when it meets the criteria of having atomic values, meaning each column contains indivisible values, and each record is unique. This eliminates repeating groups and ensures that each entry in the table is distinct, facilitating easier data management and retrieval.

All types of indexes—clustered, non-clustered, and composite—enhance query performance by organizing data in a way that allows for faster retrieval. Clustered indexes sort the data rows, non-clustered indexes create a separate structure for quick lookups, and composite indexes combine multiple columns to optimize search queries, making them all effective for frequently searched columns.

Denormalization can improve performance in specific scenarios by reducing the need for complex joins and speeding up read operations. However, it can also lead to data redundancy and anomalies, complicating updates and maintenance. Therefore, it is not always recommended and should be carefully considered based on the specific use case and requirements.

A one-to-many relationship in database design occurs when a single record in one table (Table A) can be associated with multiple records in another table (Table B). This structure allows for efficient data organization and retrieval, enabling a clear linkage between entities while maintaining data integrity across the database.

A composite key is made up of two or more columns that together uniquely identify a row in a database table. If a key consists of only a single column, it is not considered composite; thus, the statement is false.

Normalization is a database design technique that organizes data into smaller, related tables to reduce redundancy and improve data integrity. By breaking down large tables, it minimizes anomalies such as update, insert, and delete anomalies, ensuring that the database remains efficient and consistent. This process enhances data management and retrieval.

Indexes enhance database performance by allowing the database engine to quickly locate and access the data needed for queries. This leads to faster query execution times and improved search performance, as indexes reduce the amount of data that must be scanned to find relevant records. However, they do not inherently reduce disk space usage or provide automatic backups.