1. What is the primary goal of denormalization in a database?

2. How does denormalization differ from normalization in database design?

3. What is one of the key principles of denormalization?

4. In which scenarios is denormalization most beneficial?

5. What is the primary advantage of denormalization for read-heavy workloads?

6. Which type of databases often use denormalization to optimize query performance for analytical queries?

7. What is a potential drawback of denormalization in a database?

8. What is the primary focus of denormalization when designing a database?

9. What is the term for introducing redundancy into a database for query performance optimization?

10. In denormalized databases, what is the common outcome regarding query complexity?

11. What is a common use case for denormalization in database design?

12. What happens to data redundancy in a denormalized database?

13. Which workload benefits the most from denormalization?

14. What is the main trade-off of denormalization in database design?

15. What is the primary reason for denormalizing a database for reporting purposes?

16. What is a common technique in denormalization for improving query performance?

17. Which aspect of database design is not a primary concern in denormalization?

18. What is a potential challenge when updating denormalized data in a database?

19. In which type of databases is denormalization generally not recommended?

20. What is a materialized view in the context of denormalization?

21. How does denormalization impact storage space requirements in a database?

22. What term describes a situation where data in a denormalized database becomes inconsistent?

23. What type of database systems is denormalization well-suited for?

24. What is a common goal when denormalizing for data warehousing?

25. What is a potential trade-off when denormalizing for improved query performance?

26. In which scenario would denormalization be the least beneficial?