_______ is a Dimensional Modeling technique used to handle slowly changing dimensions.
- Conforming Dimension
- Degenerate Dimension
- Junk Dimension
- Slowly Changing Dimension
Slowly Changing Dimensions (SCD) is a Dimensional Modeling technique used to handle changes in dimension attributes over time. SCDs are categorized into different types (Type 1, Type 2, etc.) based on how they manage historical changes in data, ensuring accuracy in analytical reporting.
How does partitioning contribute to storage optimization in distributed databases?
- Centralizing data storage
- Distributing data across multiple nodes
- Implementing stronger encryption
- Increasing data redundancy
Partitioning in distributed databases involves distributing data across multiple nodes. This contributes to storage optimization by allowing parallel processing, reducing load on individual nodes, and improving overall system performance. It facilitates efficient data management in large-scale distributed environments.
Scenario: A financial institution requires a data warehouse to analyze customer transactions and account balances over time. How would you utilize Dimensional Modeling principles to design the data model?
- Fact table for customers, dimensions for transactions and time
- Fact table for time, dimensions for customers and transactions
- Fact table for transactions, dimensions for customers and time
- No need for Dimensional Modeling in financial analysis
For a financial institution analyzing customer transactions and account balances, a Fact table for transactions with dimensions for customers and time is suitable. This allows for detailed analysis based on customer transactions over time.
An entity with a modality of _______ indicates that its presence is mandatory in a relationship.
- Compulsory
- Conditional
- Mandatory
- Optional
An entity with a modality of Mandatory indicates that its presence is mandatory in a relationship. This means that every instance of the entity must participate in the relationship.
How does denormalization differ from normalization in terms of database design?
- Denormalization and normalization are synonymous terms used to describe the same process in database design.
- Denormalization and normalization have no impact on query performance.
- Denormalization involves intentionally introducing redundancy into a database by combining tables to improve query performance. Normalization, on the other hand, focuses on minimizing redundancy by organizing data into separate tables and ensuring dependencies are logical.
- Denormalization is only applicable in NoSQL databases, while normalization is reserved for SQL databases.
Denormalization and normalization represent opposing strategies in database design. Denormalization intentionally introduces redundancy to enhance query performance, while normalization seeks to minimize redundancy for logical organization.
A healthcare organization is building a data warehouse to analyze patient admissions. It has a fact table containing admission dates, discharge dates, and lengths of stay. How would you design the dimension tables to support this fact table?
- Doctor information, Insurance details, Geographical data
- Patient details, Medical procedures, Time dimensions
- Room details, Equipment inventory, Employee information
- Treatment history, Billing information, Payment methods
In a healthcare scenario, dimension tables would typically include information about patients, medical procedures, and time dimensions. This supports analysis related to patient demographics, medical treatments, and temporal trends in admissions.
In denormalization, what is typically the impact on data redundancy and storage space?
- Decreases redundancy and decreases storage space
- Decreases redundancy and increases storage space
- Increases redundancy and decreases storage space
- Increases redundancy and increases storage space
In denormalization, there is typically an increase in both data redundancy and storage space. Redundancy is intentionally introduced to enhance query performance, and as a result, more storage space is consumed.
In a graph database, a _______ is a data entity represented by a node.
- Document
- Edge
- Relationship
- Vertex
In a graph database, a "Vertex" is a data entity represented by a node. A vertex typically contains properties that describe the entity, and the relationships between vertices define the connections in the graph.
Scenario: A retail store maintains a database of products and categories. Each product can belong to multiple categories, and each category can have multiple products. What type of relationship would you establish between products and categories?
- Many-to-Many
- Many-to-One
- One-to-Many
- One-to-One
The relationship between products and categories in this scenario is Many-to-Many. Each product can belong to multiple categories, and each category can have multiple products. This type of relationship is common in scenarios where entities are interconnected in a non-exclusive manner.
What is indexing in the context of relational databases?
- A data structure that enhances sorting and searching operations
- A mechanism to enforce data integrity
- A technique to encrypt sensitive data
- An approach to optimize database backup processes
Indexing in relational databases refers to the creation of a data structure that enhances sorting and searching operations. It accelerates query retrieval by providing a quick lookup mechanism, especially for large datasets, and is crucial for optimizing database performance.