When data is split into multiple tables, what is true?

When data is split into multiple tables, what is true?

• A. Each entity gets its own table
• B. All data goes into one table
• C. You remove fields
• D. It eliminates the need for keys

Answer: (A)

Organizing data into separate entities via normalization means each distinct subject gets its own table. When data is split this way, you typically create a table for each entity, such as a Customers table and an Orders table. Each table stores attributes relevant to that entity, and you use keys to link related data across tables. A primary key uniquely identifies each row in a table (like a customer_id in the Customers table). A foreign key in another table (for example, customer_id in the Orders table) references that primary key to establish the relationship between orders and the customer who placed them. This setup reduces redundancy and helps maintain data integrity. So, the statement that best fits this practice is that each entity gets its own table. The other options don’t align with splitting data: putting all data into one table defeats the purpose of normalization, removing fields isn’t the goal, and you still need keys to relate data across tables.

Organizing data into separate entities via normalization means each distinct subject gets its own table. When data is split this way, you typically create a table for each entity, such as a Customers table and an Orders table. Each table stores attributes relevant to that entity, and you use keys to link related data across tables. A primary key uniquely identifies each row in a table (like a customer_id in the Customers table). A foreign key in another table (for example, customer_id in the Orders table) references that primary key to establish the relationship between orders and the customer who placed them. This setup reduces redundancy and helps maintain data integrity.

So, the statement that best fits this practice is that each entity gets its own table. The other options don’t align with splitting data: putting all data into one table defeats the purpose of normalization, removing fields isn’t the goal, and you still need keys to relate data across tables.