In terms of data warehousing, why might a cold backup be preferable to a hot backup?
- Cold backups are faster to restore
- Cold backups capture all changes in real-time
- Cold backups do not disrupt normal operations
- Cold backups require less storage space
In data warehousing, a cold backup is preferable to a hot backup when data needs to be backed up without disrupting the normal operations of the data warehouse. Unlike hot backups, cold backups can be taken when the system is offline, making them ideal for maintaining data integrity without interruptions.
Which backup method only captures the changes since the last full backup?
- Differential Backup
- Full Backup
- Incremental Backup
- Snap Backup
The backup method that captures only the changes made since the last full backup is called an "Incremental Backup." It helps in conserving storage space and time by backing up only the data that has changed since the last backup, whether it's a full or incremental one.
In data warehousing, what is the architecture that includes a main data warehouse and smaller data marts for specific business areas?
- Data Warehouse Bus
- Data Warehouse Federation
- Data Warehouse Hierarchy
- Data Warehouse Network
In data warehousing, the architecture that includes a main data warehouse and smaller data marts for specific business areas is called the "Data Warehouse Hierarchy." This structure provides a way to organize data for different business needs while maintaining a central repository.
Why is metadata management crucial for data governance and compliance?
- It automates data backup and disaster recovery
- It ensures data privacy and encryption
- It facilitates data migration between databases
- It provides a structured catalog of data assets and their lineage
Metadata management is essential for data governance and compliance as it maintains a structured catalog of data assets, their origin, transformations, and usage. This information is critical for data lineage, ensuring data integrity, and complying with regulations by tracking data provenance and ensuring data quality.
How does the kernel function influence the representation of data in a kernel density plot?
- It determines the center of the distribution
- It determines the shape of the distribution
- It determines the skewness of the distribution
- It determines the width of the distribution
The kernel function in a kernel density plot influences the shape of the distribution. Different kernel functions can produce different shapes, potentially highlighting different features in the data.
While all three types, EDA, CDA, and Predictive Modeling involve dealing with data, _______ relies heavily on visual methods for exploring the data.
- All of them equally
- CDA
- EDA
- Predictive Modeling
EDA (Exploratory Data Analysis) relies heavily on visual methods such as plots and charts to help the analyst explore and understand the underlying structure of the data, its patterns, relationships, or any hidden trends.
What are the implications of a low standard deviation in a data set?
- The data values are close to the mean
- The data values are spread out widely from the mean
- The data values are uniformly distributed
- The data values have many outliers
A "Low Standard Deviation" implies that the data values are "Close to the mean". In other words, most data points are close to the average data point.
Kendall's Tau is commonly used for which type of data?
- Continuous data
- Interval data
- Nominal data
- Ordinal data
Kendall's Tau is commonly used for ordinal data. It measures the ordinal association between two measured quantities. Like Spearman's correlation, it's based on ranks and is a suitable measure of association for ordinal data.
What would be a potential problem when treating discrete data as continuous?
- It can improve the accuracy of a machine learning model
- It can lead to inaccurate conclusions due to incorrect statistical analyses
- It can make the data cleaning process easier
- It can simplify the data visualization process
Treating discrete data as continuous can lead to inaccurate conclusions due to incorrect statistical analyses. For example, it can affect the choice of statistical tests or machine learning models, leading to potential misinterpretation of the data.
In a dataset, the type of data that can have an infinite number of possible values within a selected range is called _____ data.
- Continuous
- Discrete
- Nominal
- Ordinal
Continuous data can take any value within a range and can be subdivided infinitely.