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.
What does the residual plot tell you in a simple linear regression analysis?
- It shows the distribution of residuals and can help identify non-linearity, unequal error variances, and outliers
- It shows the distribution of the independent variable
- It shows the relationship between the dependent and independent variables
- It tells you the strength of the correlation
A residual plot is a graph that shows the residuals on the vertical axis and the independent variable on the horizontal axis. It helps to identify non-linearity, unequal error variances (heteroscedasticity), and outliers. If the points in a residual plot are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a non-linear model is more appropriate.
How does multiple linear regression differ from simple linear regression?
- Multiple linear regression cannot handle categorical variables, simple linear regression can
- Multiple linear regression is not suitable for prediction tasks
- Multiple linear regression requires a larger dataset
- Multiple linear regression uses multiple independent variables, simple linear regression only uses one
The main difference between simple and multiple linear regression is the number of independent variables. While simple linear regression uses only one independent variable to predict the dependent variable, multiple linear regression uses two or more independent variables to predict the dependent variable.
A situation where two or more independent variables in a regression model are highly correlated is known as ________.
- autocorrelation
- heteroscedasticity
- homoscedasticity
- multicollinearity
Multicollinearity refers to a situation in which two or more independent variables in a regression model are highly linearly related. This can lead to unstable estimates of the regression coefficients and make it difficult to assess the effect of independent variables on the dependent variable.