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.
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.
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.
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.
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.
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.
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.
What are derived attributes, and why are they used in database design?
- Attributes that are mandatory
- Attributes that are not essential
- Attributes that can be calculated or derived from other attributes
- Attributes with no relation to other attributes
Derived attributes in database design are those that can be calculated or derived from other attributes in the database. They are used to avoid data redundancy and improve data accuracy by ensuring that certain values are always up-to-date based on the values of other attributes.
A _______ constraint is used to ensure that a column value meets specific criteria.
- Check
- Foreign
- Primary
- Unique
Detailed A check constraint is used to ensure that a column value meets specific criteria or conditions. This helps in maintaining data accuracy and consistency by defining rules that must be satisfied for data in a column.
What are clustering techniques used for in relational schema design?
- Creating composite keys
- Grouping related tables together on disk
- Implementing referential integrity
- Reducing data redundancy
Clustering techniques in relational schema design involve grouping related tables together on disk. This can enhance query performance by minimizing disk I/O when retrieving data from interconnected tables in a query.
A manufacturing company wants to calculate the average production output per factory location. Which data modeling technique would you recommend for this scenario?
- Entity-Relationship Diagram
- Fact and Dimension Tables
- Snowflake Schema
- Star Schema
To calculate the average production output per factory location, the recommended data modeling technique is to use Fact and Dimension Tables. This approach involves creating a fact table containing production data and dimension tables providing details about factory locations, enabling efficient analysis.
Two events are said to be ________ if the occurrence of one does not affect the probability of the occurrence of the other.
- Dependent
- Exhaustive
- Independent
- Mutually exclusive
Two events are said to be "independent" if the occurrence of one does not affect the probability of the occurrence of the other. For example, if you toss a coin twice, the outcome of the first toss doesn't affect the outcome of the second toss, so the two events are independent.