Database Management Strategies Quiz

1. What is data normalization in databases?

• Data normalization in databases is a technique used to reduce data redundancy and improve data integrity by organizing columns and tables according to a series of normal forms.
• Data normalization is a process of encrypting data for security purposes.
• Data normalization increases data redundancy by merging tables into one.
• Data normalization is solely focused on speeding up query performance without changing data structure.

2. Who proposed the relational model that includes database normalization?

• Alan Turing
• Tim Berners-Lee
• Edgar F. Codd
• Charles Babbage

3. What is the first normal form (1NF)?

• The first normal form (1NF) is when data is stored in one single table without relationships.
• The first normal form (1NF) is achieved when each cell in the database holds a single value and each record is unique.
• The first normal form (1NF) requires all values in the database to be sorted alphabetically.
• The first normal form (1NF) is reached when there are no duplicate tables in the database.

4. What is the second normal form (2NF)?

• The second normal form (2NF) is accomplished when there are no primary keys in the database.
• The second normal form (2NF) is achieved when a database is in 1NF and all non-key attributes are fully functionally dependent on the primary key.
• The second normal form (2NF) is reached when all data is stored in one table with no relationships.
• The second normal form (2NF) is met when all attributes are combined into a single value.

5. What is the third normal form (3NF)?

• The third normal form (3NF) is achieved when a database is in 2NF and there are no transitive dependencies.
• The third normal form (3NF) is achieved when there are only two tables in the database.
• The third normal form (3NF) is achieved when a database has multiple primary keys.
• The third normal form (3NF) is achieved when all records in the database are unique.

6. What is Z-Score Normalization?

• Z-Score Normalization scales data to range between 0 and 1.
• Z-Score Normalization only applies to categorical data variables.
• Z-Score Normalization involves averaging all data points together.
• Z-Score Normalization centers the data with a mean of 0 and a standard deviation of 1.

7. What is Min-Max Normalization?

• Min-Max Normalization reduces the size of the database by deleting records.
• Min-Max Normalization creates duplicate data by merging tables into one.
• Min-Max Normalization adjusts data by sorting it in descending order.
• Min-Max Normalization scales the data to a specific range, such as [0.0, 1.0], using the minimum and maximum values of the data.

8. What is Decimal Scaling Normalization?

• Decimal Scaling Normalization centers the data around a mean of 0 with a standard deviation of 1.
• Decimal Scaling Normalization removes outliers by setting values outside a specified range to zero.
• Decimal Scaling Normalization moves the decimal point of values to result in values typically in the range of -1 to 1.
• Decimal Scaling Normalization applies a fixed value to each entry to ensure uniform distribution.

9. What are the common normal forms used in database normalization?

• Fourth Normal Form (4NF), Fifth Normal Form (5NF), and Sixth Normal Form (6NF)
• Simple Normal Form (SNF), Regular Normal Form (RNF), and Complex Normal Form (CNF)
• First Normal Form (1NF), Second Normal Form (2NF), and Third Normal Form (3NF)
• Basic Normal Form (BNF), Extended Normal Form (ENF), and Advanced Normal Form (ANF)

10. What is the Boyce-Codd Normal Form (BCNF)?

• The Boyce-Codd Normal Form (BCNF) is a developed version of the 3rd normal form data model (3NF) where there are no candidate keys that overlap.
• The Boyce-Codd Normal Form (BCNF) is a normalization process that allows for transitive dependencies in database relations.
• The Boyce-Codd Normal Form (BCNF) is a stage that ensures all data is duplicated across tables without exceptions.
• The Boyce-Codd Normal Form (BCNF) is a design principle that emphasizes minimal table usage in data storage.

11. What are the benefits of data normalization?

• Increasing data duplication
• Obscuring data relationships
• Reducing data redundancy
• Enhancing data inconsistency

12. What are the potential challenges in scaling up data connections?

• Decreased processing power
• Increased physical storage
• Simplified network configuration
• Potential bottlenecks

13. What is the purpose of foreign key constraints in data normalization?

• Foreign key constraints limit the number of records in a table.
• The purpose of foreign key constraints is to ensure consistent relationships between tables.
• Foreign key constraints increase the speed of data retrieval.
• Foreign key constraints prevent duplicate rows in a table.

14. How does data normalization reduce data anomalies?

• Data normalization complicates query performance without improving data integrity.
• Data normalization increases data duplication by merging related information into fewer tables.
• Data normalization reduces data anomalies by eliminating partial dependencies and transitive dependencies, ensuring that each piece of data is associated with the primary key.
• Data normalization has no effect on data integrity and focuses only on storage.

15. What is the trade-off between query performance time and storage in normalized data?

• Query performance time decreases when using fewer tables.
• Storage costs may increase due to data duplication.
• Query performance time may increase due to multiple table joins.
• Normalization eliminates the need for tables altogether.

16. Why is it important to have a thorough understanding of data normal forms and structures?

• Familiarity with data normal forms prevents software bugs that may arise during the coding process of a database.
• It is important to have a thorough understanding of data normal forms and structures to properly standardize data and avoid significant data anomalies like inconsistent dependencies.
• Knowing data normal forms helps in developing programming languages that can handle large datasets with ease.
• Understanding data normal forms enhances graphic design in databases to improve aesthetics and usability.

17. What happens if the initial process of data normalization is incorrect?

• It will automatically backup the entire database.
• It can lead to significant data anomalies like inconsistent dependencies.
• It prevents the creation of any new tables.
• It can improve overall system performance instantly.

18. What are some common data normalization techniques used in data analysis and machine learning?

• Linear Regression
• Ensemble Learning
• Decision Trees
• Min-Max Normalization

19. How is the Iris dataset used to demonstrate data normalization techniques?

• The Iris dataset is used to demonstrate data normalization techniques through manual calculations without applying statistical methods.
• The Iris dataset is used to demonstrate data normalization techniques by comparing various datasets without normalization processes.
• The Iris dataset is used to demonstrate data normalization techniques such as Z-Score Normalization by applying these techniques to real-world data and observing their effects on data transformation.
• The Iris dataset is used to demonstrate data normalization techniques by ignoring scaling and focusing solely on clustering.

20. What is the purpose of creating a separate table for subsets of data that can be placed in multiple rows?

• To reduce redundancy and ensure data association.
• To combine unrelated data into a single table.
• To increase data storage and complexity.
• To eliminate all data from the database.

21. What is the difference between 2NF and 3NF?

• The difference between 2NF and 3NF is that 2NF allows partial dependencies while 3NF does not allow any dependencies.
• The difference between 2NF and 3NF is that 2NF requires primary keys to be composite, while 3NF only requires single primary keys.
• The difference between 2NF and 3NF is that 2NF focuses on data formatting while 3NF focuses on data processing.
• The difference between 2NF and 3NF is that 2NF ensures all non-key attributes are fully functionally dependent on the primary key, while 3NF ensures there are no transitive dependencies.

22. What are some common data anomalies that can occur if normalization rules are violated?

• Unindexed columns
• Non-unique records
• Partial dependencies
• Data duplication

23. How does data normalization enhance referential integrity?

• Data normalization improves referential integrity by duplicating data across multiple tables for faster access.
• Data normalization enhances referential integrity by organizing related information into distinct tables and using foreign key constraints to ensure consistent relationships.
• Data normalization decreases referential integrity by merging tables and eliminating foreign key constraints.
• Data normalization has no effect on referential integrity, as it focuses solely on data redundancy.

24. What is the significance of the mean and standard deviation in Z-Score Normalization?

• They change the data type to integers.
• They eliminate outliers from the data set.
• They limit the data to a specific range only.
• They center the data to standardize it.

25. How does data normalization reduce storage costs?

• Data normalization reduces storage costs by eliminating redundant data and organizing information into more efficient structures.
• Data normalization keeps all data in a single large table to avoid complexity.
• Data normalization increases storage costs by adding more tables and relationships.
• Data normalization requires more disk space to keep track of relationships between data.

26. What are some practical examples of data normalization in databases?

• Creating a single table for all information
• Storing duplicate entries in the database
• Merging different data types into one column
• Splitting non-atomic values into multiple records to achieve 1NF

27. What is the role of foreign keys in data normalization?

• Foreign keys serve only as unique identifiers for each record in a single table.
• Foreign keys eliminate the need for tables by combining data into one.
• The role of foreign keys in data normalization is to link related information in different tables, ensuring consistent relationships and referential integrity.
• Foreign keys increase data redundancy by duplicating information across tables.

28. How does data normalization impact query performance?

• Data normalization has no effect on query performance at all.
• Data normalization speeds up all queries by combining tables.
• Data normalization decreases data integrity, leading to faster queries.
• Data normalization can impact query performance by increasing the time it takes to scan multiple tables.

29. What are some common applications of data normalization in real-world scenarios?

• Deleting customer interactions
• Organizing employee data
• Ignoring product specifications
• Combining multiple datasets without structure

30. What are some potential challenges in managing distributed systems during data normalization?

• Uniform physical hardware across all locations.
• Complexity in maintaining data consistency across multiple systems.
• Eliminated need for backups during normalization.
• Decreased network bandwidth across local areas.