Data Warehousing - Database MCQ Questions and answers- Technical Aptitude

In the context of data warehousing and database design, fact tables are typically structured to be completely denormalized. Denormalization is a process where data is intentionally organized in a way that reduces or eliminates the need for joins between tables, often at the expense of redundancy. This is done to improve query performance in data warehousing environments, where analytical queries are common.

Here's an explanation of the other options:

Option B: Partially denormalized doesn't accurately describe fact tables. While some degree of denormalization may occur, the term "completely denormalized" is a more appropriate characterization.

Option C: Completely normalized is not typically the case for fact tables in data warehousing. Normalization involves minimizing data redundancy by organizing data into separate tables based on relationships, which is the opposite of what is done in a fact table.

Option D: Partially normalized also doesn't accurately describe fact tables. Fact tables are intentionally denormalized for performance reasons, so "completely denormalized" is the more suitable term.

So, the correct answer is Option A: Completely denormalized. Fact tables in data warehousing are often structured in a way that optimizes query performance, even if it means duplicating data to reduce the need for complex joins.

Data transformation involves various processes that include:

Changing data from a detailed level to a summary level: This process is a key aspect of data transformation where detailed data is aggregated or summarized to provide a higher-level view.

This process helps in creating summaries and reports that are useful for analysis and decision-making.

Other options involve joining or separating data, which are related to data integration and partitioning, not specifically to data transformation.