What are the key considerations when designing a conceptual schema?
- Data integrity, simplicity, and normalization
- Data redundancy, complexity, and denormalization
- Normalization, redundancy, and write efficiency
- Query performance, redundancy, and complexity
Key considerations when designing a conceptual schema include maintaining data integrity, ensuring simplicity, and applying normalization techniques. These factors contribute to a robust and efficient database design.
In database modeling, a _______ key is a key that uniquely identifies a record within a table.
- Composite
- Foreign
- Primary
- Secondary
In database modeling, a primary key is a key that uniquely identifies a record within a table. It serves as the unique identifier for each row and ensures data integrity by preventing duplicate or null values in this key field.
_______ is the process of distributing data across multiple servers in a NoSQL database.
- Data Aggregation
- Data Fragmentation
- Data Replication
- Data Sharding
Sharding is the process of distributing data across multiple servers in a NoSQL database. It helps in improving performance and scalability by dividing the dataset into smaller, manageable parts that can be processed independently.
Partitioning based on _______ involves dividing data based on specific ranges of values.
- Attributes
- Columns
- Entities
- Relationships
Partitioning based on Attributes involves dividing data based on specific ranges of values. This technique is commonly used to organize and manage large datasets efficiently, improving query performance and data retrieval.
One key feature of document-based databases is _______ consistency, which allows for efficient distributed data management.
- Causal
- Eventual
- Immediate
- Strong
One key feature of document-based databases is eventual consistency. This consistency model prioritizes availability and partition tolerance in distributed systems, ensuring that all nodes eventually reach a consistent state despite potential network delays or failures. This makes document-based databases efficient for distributed data management in scenarios where real-time consistency is not a strict requirement.
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.
Effective collaboration in data modeling requires clear _______ among team members.
- Algorithms
- Coding skills
- Communication
- Data structures
Clear communication is crucial for effective collaboration in data modeling. It ensures that team members understand each other's perspectives, requirements, and decisions, promoting a cohesive and efficient modeling process.