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.
In a time dimension, which of the following can be considered a hierarchy?
- Customer Addresses
- Employee IDs
- Product Names
- Years, Months, Days
In a time dimension, a hierarchy typically consists of time-related attributes like Years, Months, and Days. These attributes form a natural hierarchical structure in the context of time, enabling drill-down or roll-up analysis, which is common in data warehousing for time-based reporting and analysis.
The process of combining two or more data sources into a single, unified view is known as _______.
- Data Aggregation
- Data Convergence
- Data Harmonization
- Data Integration
Explanation:
Which OLAP operation involves viewing the data cube by selecting two dimensions and excluding the others?
- Dicing
- Drilling
- Pivoting
- Slicing
In OLAP (Online Analytical Processing), the operation of viewing the data cube by selecting two dimensions while excluding others is known as "Dicing." Dicing allows you to focus on specific aspects of the data cube to gain insights into the intersection of chosen dimensions.
Which of the following is NOT typically a function of ETL tools?
- Data Analysis
- Data Extraction
- Data Loading
- Data Transformation
ETL tools are primarily responsible for data Extraction, Transformation, and Loading (ETL). Data Analysis is typically not a function of ETL tools. Data analysis is performed using BI (Business Intelligence) tools or other analytics platforms after the data has been loaded into the data warehouse.
Which schema design is characterized by a central fact table surrounded by dimension tables?
- Hierarchical Schema
- Relational Schema
- Snowflake Schema
- Star Schema
A Star Schema is characterized by a central fact table that contains numerical performance measures (facts) and is surrounded by dimension tables that describe the dimensions associated with the facts. This design is commonly used in data warehousing to simplify query performance and reporting.
Why might an organization consider using a Data Warehouse Appliance?
- To accelerate data analytics and reporting
- To replace traditional file servers
- To save electricity costs
- To store unstructured data
An organization might consider using a Data Warehouse Appliance to accelerate data analytics and reporting. These appliances are purpose-built for data warehousing, offering high-speed data processing and storage capabilities, making them ideal for organizations seeking to improve the speed and efficiency of their data analysis and reporting processes.
In a data warehouse, a _______ is a large, subject-oriented, integrated, time-variant, and non-volatile collection of data that supports decision-making.
- Data Cube
- Data Lake
- Data Mart
- Data Warehouse
In a data warehouse, a Data Warehouse is a large, subject-oriented, integrated, time-variant, and non-volatile collection of data that supports decision-making. It is designed to provide a centralized repository of historical data for reporting and analysis.