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.
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.
In the context of data warehousing, what does the ETL process stand for?
- Efficient Transfer Logic
- Enhanced Table Lookup
- Extract, Transfer, Load
- Extract, Transform, Load
In data warehousing, ETL stands for "Extract, Transform, Load." This process involves extracting data from source systems, transforming it into a suitable format, and loading it into the data warehouse. Transformation includes data cleansing, validation, and structuring for analytical purposes.
A method used in data cleaning where data points that fall outside of the standard deviation or a set range are removed is called _______.
- Data Normalization
- Data Refinement
- Data Standardization
- Outlier Handling
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.
In the context of BI tools, what does "self-service" typically refer to?
- Business users independently accessing and analyzing data
- Data security measures in place
- IT departments controlling all data access
- Users creating their own data silos
"Self-service" in the context of BI tools typically refers to business users having the capability to independently access and analyze data without requiring constant IT intervention. This empowers end-users to perform ad-hoc reporting and analysis, reducing their reliance on IT for data-related tasks.
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.
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.
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.
An organization has data scattered across multiple databases and wants to create a unified, reliable repository for business intelligence and reporting. Which solution would be most apt?
- Data Lake
- Data Mart
- Data Warehouse
- ETL (Extract, Transform, LoaProcess
A Data Warehouse is the most appropriate solution in this scenario. It's designed to integrate data from various sources, ensuring data consistency, reliability, and a unified repository for business intelligence and reporting purposes. Data Marts, Data Lakes, and ETL processes are components often used within a Data Warehouse environment.
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.
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.