To answer this question, we need to understand the concept of data normalization and its relationship to data warehousing.

Data normalization is a process in database design that organizes data into tables to eliminate redundancy and improve data integrity. It involves breaking down data into smaller, more manageable tables and establishing relationships between them.

A data warehouse is a large, centralized repository of data that is used for reporting and analysis. It is designed to support decision-making processes by providing a consolidated view of data from various sources.

Option A) Can be normalized but often isn't - This option is correct because data in a data warehouse can be normalized, but it is not always necessary or practical. In many cases, data in a data warehouse is denormalized for performance reasons. Denormalization involves combining data from multiple tables into a single table or duplicating data in order to improve query performance.

Option B) In a flat file format - This option is incorrect because data in a data warehouse is typically stored in a structured format, such as a relational database, rather than a flat file format. Flat files are generally not suitable for storing large amounts of data for analytical purposes.

Option C) Must be in normalized form to at least 2NF - This option is incorrect because there is no strict requirement for data in a data warehouse to be in normalized form. While normalization is a best practice in database design, it is not always practical or necessary in a data warehousing context.

Option D) Must be in normalized form to at least 3NF - This option is incorrect for the same reasons as option C. There is no strict requirement for data in a data warehouse to be in normalized form to at least 3NF.

The correct answer is option A) Can be normalized but often isn't. This option is correct because data in a data warehouse can be normalized, but it is not always necessary or practical due to performance considerations.

• Thanks
• Wrong explanation