_______ 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.
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.
A strategy that involves making copies of the data warehouse at regular intervals to minimize data loss in case of failures is known as _______.
- Data Cleansing
- Data Erosion
- Data Purging
- Data Replication
Data replication is a strategy in data warehousing that involves creating copies of the data warehouse at regular intervals. This approach helps minimize data loss in case of failures by ensuring that there are up-to-date backup copies of the data readily available. Data replication is essential for data resilience and disaster recovery.
The process of cleaning and enhancing the data so it can be loaded into a data warehouse is known as what?
- Data Extraction
- Data Integration
- Data Loading
- Data Transformation
The process of cleaning, transforming, and enhancing the data to prepare it for loading into a data warehouse is called "Data Transformation." During this phase, data is cleansed, structured, and enriched to ensure its quality and consistency for analysis.
In a top-down approach to building a data infrastructure, which is typically built first?
- Data Integration
- Data Marts
- Data Sources
- Data Warehouses
In a top-down approach to building a data infrastructure, data sources are typically the first components to be addressed. Data sources include various systems and databases that store raw data, and they need to be integrated and processed to feed into data warehouses and data marts. Starting with data sources is fundamental to ensuring data quality and consistency.
In a sales data model, which hierarchy is most likely to be used to analyze sales trends?
- Customer Hierarchy
- Location Hierarchy
- Product Hierarchy
- Time Hierarchy
In a sales data model, the Time Hierarchy is crucial for analyzing sales trends. It allows analysts to explore sales data over different time periods, such as daily, monthly, or yearly, to identify patterns, seasonality, and trends. This hierarchy helps in time-based analysis, forecasting, and decision-making.
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:
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.
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.
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.
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 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.