Which statement about de-normalized data sets is correct?

Which statement about de-normalized data sets is correct?

• A. They reduce data redundancy
• B. They increase data redundancy
• C. They are only used for storage
• D. They cannot be used for analysis

Answer: (B)

Denormalized data sets are designed to speed up data retrieval by duplicating data across the schema. This intentional duplication increases redundancy because the same pieces of information appear in multiple places. The trade-off is that while queries become faster and simpler to execute, there’s more data to maintain and a higher risk of inconsistencies if updates aren’t carefully handled. This balance is why denormalization is common in analytics and data warehousing, where read performance is prioritized. So the statement that best fits is that denormalized data sets increase data redundancy. The other ideas—reducing redundancy, being only for storage, or being unusable for analysis—don’t align with how denormalization works and why it’s used.

Denormalized data sets are designed to speed up data retrieval by duplicating data across the schema. This intentional duplication increases redundancy because the same pieces of information appear in multiple places. The trade-off is that while queries become faster and simpler to execute, there’s more data to maintain and a higher risk of inconsistencies if updates aren’t carefully handled. This balance is why denormalization is common in analytics and data warehousing, where read performance is prioritized.

So the statement that best fits is that denormalized data sets increase data redundancy. The other ideas—reducing redundancy, being only for storage, or being unusable for analysis—don’t align with how denormalization works and why it’s used.