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.
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:
How does the snowflake schema differ from the star schema in terms of its structure?
- Snowflake schema has fact tables with fewer dimensions
- Snowflake schema is more complex and difficult to maintain
- Star schema contains normalized data
- Star schema has normalized dimension tables
The snowflake schema differs from the star schema in that it is more complex and can be challenging to maintain. In a snowflake schema, dimension tables are normalized, leading to a more intricate structure, while in a star schema, dimension tables are denormalized for simplicity and ease of querying.
During which phase of the ETL process is data typically cleaned and validated?
- Execute
- Extract
- Load
- Transform
Data cleaning and validation usually take place during the "Transform" phase of the ETL process. In this stage, data is cleaned, transformed, and enriched to ensure its quality and relevance for the intended use.
For a dimension where the historical data is not tracked and only the current value is retained, which type of Slowly Changing Dimension (SCD) is implemented?
- SCD Type 1
- SCD Type 2
- SCD Type 3
- SCD Type 4
In cases where only the current value is retained in a dimension and historical data is not tracked, you would implement a Slowly Changing Dimension (SCD) Type 1. This type overwrites the existing data with the new data without maintaining a history.
A _______ is a large-scale data storage architecture that is specially designed to store, manage, and retrieve massive amounts of data.
- Data Cube
- Data Lake
- Data Silo
- Data Warehouse
A "Data Lake" is a large-scale data storage architecture designed to store, manage, and retrieve vast amounts of data. Unlike traditional databases, a data lake can accommodate both structured and unstructured data, making it a valuable asset in big data environments.