Your data warehouse system alerts show frequent memory overloads during peak business hours. What could be a maintenance strategy to address this?
- Add more data storage capacity
- Implement data partitioning
- Increase CPU processing power
- Upgrade network bandwidth
To address memory overloads in a data warehouse, implementing data partitioning is a strategic maintenance strategy. Data partitioning involves dividing large tables into smaller, more manageable segments. This can reduce the memory requirements and improve query performance during peak hours.
_______ 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.
In predictive analytics, what method involves creating a model to forecast future values based on historical data?
- Descriptive Analytics
- Diagnostic Analytics
- Prescriptive Analytics
- Time Series Forecasting
Time series forecasting is a predictive analytics method that focuses on modeling and forecasting future values based on historical time-ordered data. It is commonly used in various fields, including finance, economics, and demand forecasting.
The methodology that emphasizes a phased approach to deploying ERP solutions, where each phase is a stepping stone for the next, is called _______.
- Agile Approach
- Incremental Approach
- Iterative Approach
- Waterfall Approach
The methodology that emphasizes a phased approach to deploying ERP solutions, where each phase builds on the previous one, is called the "Incremental Approach." In this approach, each phase is a stepping stone toward achieving the final ERP solution, ensuring a structured and manageable implementation.
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.