_______ is a technique used in data warehouses to determine the order in which data is physically stored in a table, often to improve query performance.
- Data Cleaning
- Data Clustering
- Data Modeling
- Data Sorting
Data clustering is a technique used in data warehouses to determine the physical order of data within a table. It is done to group similar data together, optimizing query performance by reducing the need to access scattered data.
A _______ provides a consolidated and consistent view of data sourced from various systems across an organization.
- Data Mart
- Data Mining
- Data Source
- Data Warehouse
A Data Warehouse provides a consolidated and consistent view of data sourced from various systems across an organization. It is designed to support data analysis and reporting by providing a centralized repository for structured data from different sources.
What is the primary goal of Business Intelligence (BI)?
- Generating Reports
- Managing Payroll
- Predicting Future Profits
- Providing Data Insights
The primary goal of Business Intelligence (BI) is to provide data insights and support decision-making. BI systems gather, process, and analyze data to help organizations gain a deeper understanding of their business and make informed choices based on data-driven insights.
After profiling a dataset, a data analyst discovers that multiple columns have the same values in the same order, but with different column names. What should be the next step in the data cleaning process?
- Combine the columns into a single column
- Drop one of the columns
- Leave them as they are
- Rename the columns to have the same name
In this situation, you should rename the columns to have the same name. It ensures consistency and clarity in the dataset, making it easier to work with. This step is crucial for data integration and analysis as it avoids redundancy and confusion that might arise from having multiple column names for the same data.
In the context of ERP, what is the primary challenge of "data silos"?
- Data accessibility and integration
- Data backup
- Data security
- Efficient data storage
The primary challenge of "data silos" in the context of ERP (Enterprise Resource Planning) is ensuring that data is accessible and integrated across various departments and modules within the organization. Data silos result in isolated information that can hinder effective decision-making and collaboration. Integrating data from different sources is essential for ERP to deliver its full benefits.
Why might a database administrator choose to denormalize a database?
- To optimize data storage and retrieval performance
- To reduce data redundancy and improve data consistency
- To reduce redundancy and improve data consistency
- To simplify the database structure and improve data integrity
A database administrator may choose to denormalize a database to optimize data storage and retrieval performance. Denormalization involves reducing the number of tables and increasing redundancy, which can speed up query performance, particularly in data warehousing where complex queries are common. However, it may come at the cost of some data integrity and consistency.
An e-commerce company is designing a data model for their sales. They have measures like "Total Sales" and "Number of Items Sold." They want to analyze these measures based on categories like "Product Type," "Brand," and "Region." Which elements in their model would "Product Type," "Brand," and "Region" be considered as?
- Aggregations
- Dimensions
- Fact Tables
- Measures
"Product Type," "Brand," and "Region" are considered dimensions in the data model. Dimensions are attributes used for analyzing and categorizing data, while measures (like "Total Sales" and "Number of Items Sold") represent the numeric values to be analyzed.
A data scientist notices that a dataset has a few values that are significantly higher than the others, skewing the results. What transformation technique might they consider to stabilize the variances?
- Log Transformation
- Min-Max Scaling
- Outlier Removal
- Standardization (Z-score normalization)
When dealing with a dataset containing significantly higher values that skew results, log transformation is a valuable technique. It compresses the range of values, making it easier to manage extreme values and stabilize variances. This is particularly useful in scenarios like financial data analysis or when dealing with data with a heavy right-skew.
An organization wants to update its data warehouse with daily sales data. The sales data is vast, but only a small portion changes daily. Which data load approach would be most efficient?
- Full Load
- Incremental Load
- Real-time Load
- Snapshot Load
For updating a data warehouse with daily sales data where only a small portion changes daily, the most efficient approach is an incremental load. Incremental loading involves only loading the changed or new data, reducing the processing time and system resources required compared to a full load. It is suitable for efficiently updating large datasets with minimal changes.
Which of the following best describes the term "risk appetite" in IT risk management?
- The ability to predict future IT risks accurately
- The level of tolerance for spicy food in the IT department
- The organization's readiness to accept and manage IT risks to achieve its objectives
- The willingness to take risks in IT projects
"Risk appetite" in IT risk management refers to an organization's preparedness to accept and manage IT risks in pursuit of its goals and objectives. It involves assessing the balance between risk-taking and risk aversion in IT decision-making.