What is the difference between functional dependency and multi-valued dependency?
- Functional dependency and multi-valued dependency are terms used interchangeably to describe the same concept.
- Functional dependency captures the relationship between attributes within a single table, ensuring unique determinants for other attributes. Multi-valued dependency, on the other hand, deals with situations where one attribute uniquely determines another, but multiple values can exist for the same determinant.
- Functional dependency only applies to numeric attributes, while multi-valued dependency is exclusive to alphanumeric attributes.
- Functional dependency signifies a one-to-one relationship, while multi-valued dependency implies a many-to-many relationship.
Functional dependency and multi-valued dependency are distinct concepts. Functional dependency deals with one-to-one relationships within a table, whereas multi-valued dependency handles situations where one attribute uniquely determines another, allowing for multiple values for the same determinant.
What role does metadata play in version control for data modeling?
- Metadata helps in tracking changes made by users
- Metadata is irrelevant in version control
- Metadata is used only for documentation purposes
- Metadata only stores information about the latest version
Metadata plays a crucial role by helping in tracking changes made by users. It provides information about modifications, contributors, and timestamps, facilitating effective version control and collaboration in data modeling projects.
The _______ function is used to calculate the total of a numeric column in SQL.
- AVG
- COUNT
- MAX
- SUM
The SUM function in SQL is used to calculate the total of a numeric column. It adds up all the values in the specified column, providing a consolidated sum that can be useful in various analytical scenarios.
What strategies can be employed for handling changing dimensions in Dimensional Modeling?
- Adding new records with new keys
- All of the above
- Creating separate tables for historical data
- Overwriting existing data
Various strategies can be employed for handling changing dimensions, including overwriting existing data, adding new records with new keys, and creating separate tables for historical data. The choice depends on the specific requirements of the business and the nature of the dimension changes.
Scenario: A social media platform needs to ensure that all users see the most recent posts made by their friends. Which consistency model would you recommend for their NoSQL database?
- Bounded Staleness
- Causal Consistency
- Eventual Consistency
- Strong Consistency
For a social media platform prioritizing consistency, Strong Consistency is recommended. This ensures that all users see the most recent posts made by their friends without any delay or inconsistency across different nodes of the database.
Scenario: An online store has customers and orders. Each customer can place multiple orders, but an order must belong to one customer. What cardinality and modality does this scenario illustrate?
- Many-to-Many, Optional
- Many-to-One, Optional
- One-to-Many, Mandatory
- One-to-One, Mandatory
This scenario illustrates a One-to-Many relationship with mandatory modality. Each customer can place multiple orders (Many), but each order must belong to one customer (One). The modality is mandatory because every order must be associated with a customer.
In a distributed database system, _______ partitioning involves replicating data across multiple nodes.
- Hash
- Range
- Replication
- Vertical
In a distributed database system, replication partitioning involves copying or duplicating data across multiple nodes. This is done to enhance fault tolerance and improve data availability by having redundant copies of the data on different nodes within the distributed environment.
Scenario: A company is migrating its existing database to a new system. Explain how forward engineering capabilities in ER diagram tools can facilitate this process.
- Automatically transfer data from the old to the new system
- Create a reverse engineering model
- Generate SQL scripts to create the new database based on the ER diagram
- Optimize database performance
Forward engineering in ER diagram tools involves generating SQL scripts based on the ER diagram. This helps in creating the new database structure. It ensures that the design represented in the ER diagram is implemented accurately in the new system. This feature simplifies the migration process and minimizes the risk of errors during the transition.
How does clustering contribute to data storage optimization?
- By compressing data files
- By creating redundant copies of data
- By encrypting data files
- By organizing similar data together on disk
Clustering in the context of database design refers to the arrangement of similar data together on disk. This contributes to data storage optimization as it reduces the amount of I/O operations needed to access related data, enhancing query performance and storage efficiency.
What are some advantages of using a graph database over a traditional relational database in certain scenarios?
- Better support for tabular data
- Improved performance for complex relationship queries
- Lack of scalability
- Reduced storage requirements
Using a graph database offers advantages like improved performance for complex relationship queries. Graph databases excel in scenarios where relationships play a crucial role, providing faster and more efficient traversal of interconnected data compared to traditional relational databases.