Business Intelligence Quiz Questions And Answers
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Which of the following BI technique can predict value for a specific data item attribute?
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Predictive modeling
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Modeling
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Predictive value
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Association
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Do you know about business intelligence and how it works? Attempt all these questions giving correct answers based on BI (business intelligence) in this quiz. This quiz aims to test your knowledge and educate you more about business intelligence with its challenges and issues. BI refers to the common business strategies and technologies used to help the organization make more-data See moredriven decisions. However, there are a few challenges of BI that any business might face. Do you know about each of them? Play the quiz and check it now.
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- 2.
What does a typical BI environment comprise of?
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Data warehouse
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Data mart
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OLAP TOOLS
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All of the above
Correct Answer
A. All of the aboveExplanation
A typical BI environment comprises of a data warehouse, data mart, and OLAP tools. A data warehouse is a central repository that stores structured and organized data from various sources. A data mart is a subset of a data warehouse that focuses on specific business functions or departments. OLAP tools (Online Analytical Processing) allow users to analyze data from different dimensions and perspectives. Therefore, all of these components are essential in a typical BI environment to store, organize, and analyze data effectively.Rate this question:
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- 3.
Which of the following are direct benefits of Business Intelligence?
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Decision making
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Delivers data mining functionality
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Artificial intelligence
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All of the above
Correct Answer
A. Decision makingExplanation
Business Intelligence provides direct benefits for decision making by providing accurate and timely information for informed decision making. It helps in analyzing and interpreting data, identifying trends and patterns, and making data-driven decisions. Business Intelligence also enables organizations to deliver data mining functionality by extracting valuable insights from large datasets. However, Artificial Intelligence is not a direct benefit of Business Intelligence, as AI is a separate technology that can be integrated with BI tools to enhance their capabilities.Rate this question:
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- 4.
What is Data-Based Knowledge
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Knowledge derived from data through the use of Business Intelligence Tools and the process of Data Warehousing
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Knowledge derived from data through the use of Business Intelligence Tools
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Knowledge derived from data only
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Both a and c
Correct Answer
A. Knowledge derived from data through the use of Business Intelligence Tools and the process of Data WarehousingExplanation
Data-Based Knowledge refers to knowledge that is derived from data using Business Intelligence Tools and the process of Data Warehousing. Business Intelligence Tools are software applications that help in analyzing and visualizing data, while Data Warehousing is the process of collecting, storing, and managing large amounts of data. By using these tools and processes, organizations can extract valuable insights and knowledge from their data, which can be used for decision-making and improving business performance. Therefore, the correct answer is "Knowledge derived from data through the use of Business Intelligence Tools and the process of Data Warehousing."Rate this question:
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- 5.
Which of the following is/are correct types of data?
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Semi-structured
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Unstructured data
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Both a and b
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Semi data
Correct Answer
A. Both a and bExplanation
Both semi-structured and unstructured data are correct types of data. Semi-structured data refers to data that does not conform to a rigid structure like traditional databases but still has some organization and can be tagged or categorized. On the other hand, unstructured data refers to data that has no predefined structure or organization, such as text documents, images, videos, social media posts, etc. Both types of data are important in various fields, including data analysis, machine learning, and artificial intelligence, as they provide valuable insights and information.Rate this question:
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- 6.
What are the challenges to developing BI with semi-structured or unstructured data?
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Unstructured data is stored in a huge variety of formats
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There is a need to develop a standardized terminology
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Both a and b
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Problem of format and terminology is just with unstructured data and not semi structured data.
Correct Answer
A. Both a and bExplanation
The challenges to developing BI with semi-structured or unstructured data include the fact that unstructured data is stored in a huge variety of formats, making it difficult to extract and analyze. Additionally, there is a need to develop a standardized terminology to ensure consistency and accuracy in the analysis of this type of data. Both of these challenges apply to both semi-structured and unstructured data.Rate this question:
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- 7.
Often, Where do the BI applications gather data from?
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Data warehouse
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Data mart
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Both a and b
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Database
Correct Answer
A. Both a and bExplanation
BI applications gather data from both a data warehouse and a data mart. A data warehouse is a central repository that stores data from various sources and is used for reporting and analysis purposes. On the other hand, a data mart is a subset of a data warehouse that focuses on a specific department or business function. By gathering data from both a data warehouse and a data mart, BI applications can provide a comprehensive view of the organization's data and enable users to make informed decisions.Rate this question:
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- 8.
Which of the following are benefits or use of BI?
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With BI, firms can identify their most profitable customers
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Quickly detect warranty-reported problems to minimize the impact of
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Data mining
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Both a and b
Correct Answer
A. Both a and bExplanation
The correct answer is "Both a and b". BI, or Business Intelligence, offers several benefits and uses. One of them is the ability to identify a firm's most profitable customers, allowing the company to focus on retaining and satisfying them. Additionally, BI enables firms to quickly detect warranty-reported problems, minimizing their impact on the business. Therefore, both of these benefits are associated with the use of BI.Rate this question:
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- 9.
All business intelligence applications require a data warehouse.
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True
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False
Correct Answer
A. FalseExplanation
This statement is false because not all business intelligence applications require a data warehouse. While data warehouses are commonly used in business intelligence to store and organize large amounts of data, there are also other methods and technologies that can be used, such as data lakes or real-time data streaming. The use of a data warehouse depends on the specific needs and requirements of the business intelligence application.Rate this question:
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- 10.
________________ in business intelligence allows huge data and reports to be read in a single graphical interface.
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Reports
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OLAP
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Dashboard
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Warehouse
Correct Answer
A. DashboardExplanation
A dashboard in business intelligence allows huge data and reports to be read in a single graphical interface. It provides a visual representation of key performance indicators and metrics, allowing users to quickly and easily analyze and interpret data. Dashboards often include charts, graphs, and other visual elements that make it easier to understand complex information at a glance. This helps decision-makers make informed choices and identify trends or patterns in the data.Rate this question:
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- 11.
Why aggregate is used in dimensional model of data warehouse?
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To take the dimension and change its granularity
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To retrieve data from an operational system
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To store data in one operational system
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All of the above
Correct Answer
A. To take the dimension and change its granularityExplanation
In a dimensional model of a data warehouse, the use of aggregate is to take a dimension and change its granularity. This means that the data is grouped or summarized at a higher level, allowing for easier analysis and reporting. By aggregating the data, it becomes possible to view trends and patterns over a larger time frame or across different dimensions. This helps in making informed decisions and gaining insights from the data.Rate this question:
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- 12.
In which approach of data warehousing, the transaction data is partitioned into facts?
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Dimensional approach
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Normalized approach
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Operational approach
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None of the above
Correct Answer
A. Dimensional approachExplanation
In the dimensional approach of data warehousing, the transaction data is partitioned into facts. This approach organizes the data into easily understandable and accessible dimensions and facts. Dimensions represent the various attributes and characteristics of the data, while facts are the measurable numerical values associated with these dimensions. By partitioning the transaction data into facts, the dimensional approach allows for efficient analysis and reporting of the data based on different dimensions.Rate this question:
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- 13.
What is a data mart?
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It is the collection of data in data warehouse
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It is the access layer of the data warehouse environment that is used to get data out to the users
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It is the process of extracting patterns from large data sets
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It is the process of extracting data
Correct Answer
A. It is the access layer of the data warehouse environment that is used to get data out to the usersExplanation
A data mart is the access layer of the data warehouse environment that is used to get data out to the users. This means that it serves as a subset of a data warehouse, containing specific data that is relevant to a particular department or team within an organization. Data marts are designed to provide easy access to information for users, allowing them to analyze and make decisions based on the data.Rate this question:
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- 14.
A star schema has what type of relationship between a dimension and fact table?
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Many-to-many
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One-to-one
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One-to-many
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All of the above
Correct Answer
A. One-to-manyExplanation
A star schema has a one-to-many relationship between a dimension and fact table. This means that for each record in the dimension table, there can be multiple records in the fact table associated with it. This type of relationship is commonly used in data warehousing and allows for efficient querying and analysis of data.Rate this question:
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- 15.
What is data scrubbing?
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A process to reject data from the data warehouse and to create the necessary indexes
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A process to load the data in the data warehouse and to create the necessary indexes
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A process to upgrade the quality of data after it is moved into a data warehouse
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A process to upgrade the quality of data before it is moved into a data warehouse
Correct Answer
A. A process to upgrade the quality of data after it is moved into a data warehouseExplanation
Data scrubbing is a process that involves upgrading the quality of data after it has been moved into a data warehouse. This process helps to identify and correct any errors, inconsistencies, or inaccuracies in the data, ensuring that the data is reliable and accurate for analysis and decision-making purposes. It may involve techniques such as data validation, data cleansing, and data transformation to improve the overall quality of the data.Rate this question:
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- 16.
Which of the following statements is/are true about Data Warehouse?
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Can be update by end user
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Contains numerous naming conventions and format
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Organized around important subject areas
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Contain only current data
Correct Answer
A. Organized around important subject areasExplanation
A data warehouse is a centralized repository of integrated data from various sources. It is organized around important subject areas, meaning that it is designed to focus on specific topics or themes that are relevant to the business. This allows for efficient data analysis and reporting. The other statements are not true about data warehouses. Data warehouses cannot be updated by end users directly, as they are typically read-only databases. They also do not necessarily contain numerous naming conventions and formats, as data is typically standardized and transformed for consistency. Lastly, data warehouses can contain historical data, not just current data.Rate this question:
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- 17.
Fact tables are
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Completely demoralized
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Partially demoralized
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Completely normalized
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Partially normalized
Correct Answer
A. Completely normalizedExplanation
Fact tables in a data warehouse are completely normalized. This means that they are designed to eliminate redundancy and improve data integrity by organizing data into separate tables based on their attributes. In a completely normalized fact table, each attribute has its own table, and the relationships between these tables are defined through primary and foreign keys. This allows for efficient storage and retrieval of data, as well as flexibility in querying and analyzing the data.Rate this question:
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- 18.
The active data warehouse architecture includes which of the following?
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At least one data mart
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Data that can extracted from numerous internal and external sources
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Near real-time updates
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All of the above
Correct Answer
A. All of the aboveExplanation
The active data warehouse architecture includes at least one data mart, which is a subset of a data warehouse that is focused on a specific business function or department. It also includes data that can be extracted from numerous internal and external sources, allowing for a comprehensive and diverse data pool. Additionally, it involves near real-time updates, ensuring that the data in the warehouse is constantly updated and reflects the most recent information. Therefore, all of the given options are included in the active data warehouse architecture.Rate this question:
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- 19.
Data warehouse will not contain which type of data from the following options given below ?
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Historical data
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Derived data
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Metadata
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Future data
Correct Answer
A. Future dataExplanation
A data warehouse is a centralized repository of integrated data from various sources, used for reporting and analysis. It is designed to support decision-making processes by providing historical data, derived data, and metadata. However, future data cannot be included in a data warehouse as it refers to data that has not yet occurred or been collected. Therefore, future data is not stored in a data warehouse.Rate this question:
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- 20.
__________is temporary location in Data Warehouse where data from source systems is copied
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A. Staging Area
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B. Standing Area
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C. Stocking Area
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D. Caching Area
Correct Answer
A. A. Staging AreaExplanation
A staging area in a data warehouse is a temporary location where data from source systems is copied. This allows for data to be cleansed, transformed, and loaded into the data warehouse in a controlled and organized manner. The staging area acts as an intermediate step between the source systems and the data warehouse, ensuring that data is properly prepared before being integrated into the warehouse.Rate this question:
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- 21.
A cube with more than three dimensions is called
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Hyper cube
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Hybrid cub
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Hierarchical cube
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None of the above
Correct Answer
A. Hyper cubeExplanation
A cube with more than three dimensions is called a hypercube. This term is used in mathematics to describe a higher-dimensional analog of a cube. A hypercube can have any number of dimensions greater than three, and each additional dimension adds another set of edges, vertices, and faces to the shape. It is a concept often used in geometry and theoretical physics to explore higher-dimensional spaces and their properties.Rate this question:
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- 22.
_______ in the data cube represent the measure of interest
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Cell
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Dimension
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Attribute
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Side
Correct Answer
A. CellExplanation
In a data cube, the "cell" represents the measure of interest. A data cube is a multidimensional representation of data, where each cell in the cube contains a specific combination of dimensions and measures. The measures in a data cube are the numerical values that represent the data being analyzed. Therefore, the correct answer is "cell" because it refers to the specific data point or value within the cube that represents the measure of interest.Rate this question:
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- 23.
_______is the act of picking a rectangular subset of a cube by choosing a single value for one of its dimensions, creating a new cube with one fewer dimension
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Dice
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Drill Up
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Slice
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Roll Up
Correct Answer
A. SliceExplanation
Slice is the correct answer because it refers to the act of selecting a rectangular subset of a cube by choosing a single value for one of its dimensions. This creates a new cube with one fewer dimension, as stated in the question.Rate this question:
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- 24.
_______operation produces a sub cube by allowing the analyst to pick specific values of multiple dimensions
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Slice
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Dice
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Roll Up
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Drill Down
Correct Answer
A. DiceExplanation
The correct answer is "Dice". The term "dice" refers to an operation that allows the analyst to select specific values of multiple dimensions and produce a sub cube. This operation is commonly used in data analysis to focus on specific subsets of data by narrowing down the dimensions.Rate this question:
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- 25.
_______________allows an analyst to rotate the cube in space to see its various faces
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Pivot
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Dice
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Roll Up
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Slice
Correct Answer
A. PivotExplanation
Pivot allows an analyst to rotate the cube in space to see its various faces. This feature is important in data analysis as it enables the analyst to view the data from different perspectives and gain insights from different angles. By pivoting the cube, the analyst can easily examine different dimensions or variables, allowing for a more comprehensive understanding of the data.Rate this question:
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- 26.
Which of the following is not a category of ETL meta data?
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Batch Metadata
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Transformation Metadata
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Process Metadata
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Load Metadata
Correct Answer
A. Load MetadataExplanation
Load metadata is not a category of ETL metadata. ETL (Extract, Transform, Load) is a process used in data warehousing to extract data from various sources, transform it to fit the desired format, and load it into a target database or data warehouse. The other three options, Batch Metadata, Transformation Metadata, and Process Metadata, are all categories of ETL metadata that are used to track and manage different aspects of the ETL process. Load metadata specifically refers to the information related to the loading of data into the target database or data warehouse.Rate this question:
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- 27.
The purpose of exploration data warehouse is to provide a foundation for
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Descriptive Analysis
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SWOT Analysis
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Statistical analysis
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Comparative Analysis
Correct Answer
A. Statistical analysisExplanation
The purpose of an exploration data warehouse is to provide a foundation for statistical analysis. This means that the data warehouse is designed to store and organize large amounts of data that can be used for statistical analysis purposes. Statistical analysis involves the collection, organization, analysis, interpretation, and presentation of data to uncover patterns, relationships, and trends. By having a well-structured data warehouse, researchers and analysts can easily access and analyze the data they need for statistical analysis, allowing them to make informed decisions and draw meaningful conclusions.Rate this question:
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- 28.
Operational data source sits between _______ and ______
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Data warehouse , web
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Dimension table, web
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Web, fact table
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Fact table, dimension table
Correct Answer
A. Data warehouse , webExplanation
The operational data source sits between the data warehouse and the web. This means that the data from the web is collected and processed by the operational data source before being stored in the data warehouse. The data warehouse is where the organized and structured data is stored for analysis and reporting purposes. The web refers to the various sources such as websites, applications, or online platforms where the data is collected from.Rate this question:
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- 29.
________refers to the ability to start at a summary number and to break that summary into a successively finer set of summarizations.
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Roll Up
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Drill Down
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Slice
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Dice
Correct Answer
A. Drill DownExplanation
Drill Down refers to the ability to start at a summary number and to break that summary into a successively finer set of summarizations. This means that with drill down, you can start with a high-level overview and then delve deeper into the data by breaking it down into more detailed subsets or categories. It allows for a more granular analysis and understanding of the data.Rate this question:
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- 30.
The task of correcting and preprocessing data is called
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Data Cleanning
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Data scrubbing
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Data loading
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Data updation
Correct Answer
A. Data CleanningExplanation
The correct answer is "Data Cleaning" because it refers to the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. This includes tasks such as removing duplicate records, handling missing values, standardizing data formats, and resolving inconsistencies. Data scrubbing is a similar term used interchangeably with data cleaning. Data loading refers to the process of importing data into a database or data warehouse, and data updation refers to the process of updating existing data with new information.Rate this question:
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- 31.
Fact table is related to ________
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Only one dimension table
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Many dimension tables
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No dimension table
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At least one dimension table
Correct Answer
A. Many dimension tablesExplanation
A fact table is related to many dimension tables. In a data warehouse, a fact table contains the quantitative and numerical data that represents the measurements or metrics of a business process or event. Dimension tables provide the context and descriptive attributes for the data in the fact table. By relating to multiple dimension tables, the fact table can capture and analyze data from different perspectives or dimensions, allowing for more comprehensive and detailed analysis of the business data.Rate this question:
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- 32.
Many fact rows are related to _________
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Many dimension rows
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Each dimension row
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At least one dimension row
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None of the above
Correct Answer
A. Each dimension rowExplanation
The correct answer is "Each dimension row". This means that every fact row is related to each dimension row. In a data model, facts represent the measurable data or metrics, while dimensions provide the context or attributes for the facts. Therefore, for each combination of dimension values, there is a corresponding fact value.Rate this question:
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- 33.
Fact table is related to each dimension table in _________ relationship.
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N:N
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1:1
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N:1
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All of the above
Correct Answer
A. N:1Explanation
The fact table is related to each dimension table in a N:1 relationship. In a N:1 relationship, multiple records from the dimension table can be associated with a single record in the fact table. This means that for every N number of records in the dimension table, there is only 1 corresponding record in the fact table. This relationship is commonly used in data warehousing and allows for efficient storage and retrieval of data.Rate this question:
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- 34.
_______ is always at the Centre of star schema.
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Fact table
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Dimension
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Attribute
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Attribute hierarchies
Correct Answer
A. Fact tableExplanation
In a star schema, the fact table is always at the center. The fact table contains the quantitative data or measurements that are being analyzed, such as sales or revenue. It is connected to multiple dimension tables, which provide context and additional information about the data in the fact table. The fact table acts as the central point of reference for the dimensions, allowing for efficient and effective analysis of the data.Rate this question:
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- 35.
Which of the following components does not belongs to star schema?
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Facts
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Dimensions
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Attribute hierarchies
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Properties
Correct Answer
A. PropertiesExplanation
In a star schema, the central component is the fact table, which contains the measurable data. Dimensions are the descriptive attributes that provide context to the facts. Attribute hierarchies define the relationships between different levels of a dimension, allowing for drill-down analysis. However, properties do not belong to a star schema. Properties are additional characteristics or attributes associated with dimensions or facts, but they are not essential components of the star schema structure.Rate this question:
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- 36.
________ uses ________schema and ___________ uses _______
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ROLAP, Snowflake, MOLAP , Data Cubes
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ROLAP, Data Cubes, MOLAP , Star,
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ROLAP, Star, MOLAP , Data Cubes
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ROLAP, Star, OLAP , snowflake
Correct Answer
A. ROLAP, Star, MOLAP , Data CubesExplanation
ROLAP (Relational Online Analytical Processing) uses a Star schema, which organizes data into a central fact table surrounded by dimension tables. This allows for efficient querying and analysis of large amounts of data. MOLAP (Multidimensional Online Analytical Processing) uses Data Cubes, which store data in a multidimensional array format. This allows for fast calculations and aggregations across multiple dimensions. Therefore, the correct answer is ROLAP, Star, MOLAP, Data Cubes.Rate this question:
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- 37.
Data Warehouse deals with _______
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Historical data
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Live data
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Future Data
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All of the above
Correct Answer
A. Historical dataExplanation
Data Warehouse deals with historical data, which refers to data that has been collected and stored over a period of time. It is used for analysis, reporting, and decision-making purposes. Live data refers to real-time or current data, which is not typically stored in a data warehouse. Future data is not a term commonly used in the context of data warehousing. Therefore, the correct answer is historical data.Rate this question:
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- 38.
Data in dependent data mart is brought from
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Operational Data Store
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Enterprise data warehouse
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Independent data mart
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RDBMS only
Correct Answer
A. Enterprise data warehouseExplanation
The data in a dependent data mart is sourced from an enterprise data warehouse. An enterprise data warehouse is a centralized repository that integrates data from various sources within an organization. It consolidates data from different systems and departments to provide a comprehensive view of the organization's data. The dependent data mart, on the other hand, is a subset of the enterprise data warehouse, focusing on specific departments or business functions. Therefore, the data in a dependent data mart is brought from the enterprise data warehouse.Rate this question:
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- 39.
Information can be
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Structured
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Unstructured
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Semi structured
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All of the above
Correct Answer
A. All of the aboveExplanation
The given answer, "All of the above," is correct because information can indeed be structured, unstructured, or semi-structured. Structured information refers to data that is organized and easily searchable, such as information stored in databases. Unstructured information, on the other hand, lacks a specific format or organization, like text documents or social media posts. Semi-structured information lies somewhere in between, having some organization but also containing unstructured elements. Therefore, all three types of information are valid possibilities.Rate this question:
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- 40.
Hybrid OLAP is the combination of
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MOLAP and Desktop OLAP
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ROLAP and Desktop OLAP
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MOLAP and ROLAP
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MOLAP, ROLAP, and Desktop OLAP
Correct Answer
A. MOLAP and ROLAPExplanation
Hybrid OLAP is a combination of MOLAP and ROLAP. MOLAP (Multidimensional OLAP) stores data in a multidimensional cube format, allowing for fast query response times and efficient data storage. ROLAP (Relational OLAP) stores data in a relational database, utilizing SQL queries to access and analyze the data. By combining these two approaches, Hybrid OLAP is able to take advantage of the strengths of both MOLAP and ROLAP, providing a flexible and efficient solution for data analysis. Desktop OLAP is not included in the combination for Hybrid OLAP.Rate this question:
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- 41.
OLTP systems are used for
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Access to operational and historical data
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Providing various add -ons for quick decision making
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Supporting day-to-day business process
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Providing complicated computations
Correct Answer
A. Supporting day-to-day business processExplanation
OLTP systems, or Online Transaction Processing systems, are designed to support day-to-day business processes. These systems are optimized for handling high volumes of transactions and providing real-time data processing. They are used to manage and track routine operations such as sales, inventory management, customer interactions, and other operational tasks. OLTP systems ensure the smooth functioning of business operations by facilitating efficient and accurate data entry, retrieval, and updating. They enable organizations to carry out their day-to-day activities seamlessly and ensure the availability of up-to-date information for decision-making.Rate this question:
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- 42.
OALP stands for
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On-Line Analytical Processing
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Operational Analytical Processing
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On-Line Access Processing
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On-Line Analytical Programming
Correct Answer
A. On-Line Analytical ProcessingExplanation
OALP stands for On-Line Analytical Processing, which refers to a technology used for analyzing data in real-time. It allows users to perform complex queries and analysis on large volumes of data stored in databases or data warehouses. OALP enables organizations to gain valuable insights, make informed decisions, and identify trends or patterns in their data. It is commonly used in business intelligence and data analytics applications to support strategic decision-making processes.Rate this question:
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- 43.
Independent data mart derives data from
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Transactional Systems
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Enterprise Data Warehouse
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Both a and b
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None of the above
Correct Answer
A. Transactional SystemsExplanation
An independent data mart is a subset of an enterprise data warehouse that is designed to serve the needs of a specific department or business unit. It is typically built using data from transactional systems, which are the systems where day-to-day business transactions are recorded. These transactional systems capture and store detailed information about individual transactions, such as sales, orders, or customer interactions. By extracting data from these transactional systems, an independent data mart can provide a focused and tailored view of the data that is relevant to a specific department or business unit's needs. Therefore, the correct answer is transactional systems.Rate this question:
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- 44.
Which of the following is not the approach to create a data warehouse?
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Enterprise Data Warehouse
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Data Marts
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Operational Data Store
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OLAP
Correct Answer
A. OLAPExplanation
OLAP (Online Analytical Processing) is not the approach to create a data warehouse. OLAP is a technology that allows users to analyze multidimensional data from different perspectives. It is used to query and analyze data from a data warehouse or data mart, but it is not the approach used to create them. The other options listed (Enterprise Data Warehouse, Data Marts, and Operational Data Store) are all approaches used in the creation of a data warehouse.Rate this question:
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- 45.
What is true about data marts
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Data marts can’t exists without data warehouse
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Data marts servers multiple departments of an organization or several data analysis problems
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Data mart must be always smaller than data warehouse in terms of amount of data
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Can exists without being connected to a data warehouse
Correct Answer
A. Can exists without being connected to a data warehouseExplanation
Data marts can exist independently without being connected to a data warehouse. Unlike data warehouses, which store large amounts of data from various sources, data marts are smaller subsets of data that are designed to serve the specific needs of individual departments or address specific data analysis problems. While data marts can be connected to a data warehouse to access additional data, they can also function autonomously, pulling data from specific sources and serving specific purposes without relying on a data warehouse.Rate this question:
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- 46.
What is the relation between measure and dimension?
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Measures are objects that represent attributes of dimension data.
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Dimensions are objects that represent attributes of measure data
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They are independent of each other.
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Measure and dimension mean the same thing in BO
Correct Answer
A. Measures are objects that represent attributes of dimension data.Explanation
Measures and dimensions are related in that measures represent attributes of dimension data. In other words, measures provide quantitative information about the dimensions. For example, in a sales dataset, the dimension could be the product category, and the measure could be the total sales amount for each category. Measures and dimensions work together to provide a comprehensive understanding of the data and allow for analysis and reporting.Rate this question:
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- 47.
A class in BO is a collection of _______________
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Measure
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Variables
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Data
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Objects
Correct Answer
A. ObjectsExplanation
A class in BO is a collection of objects. In object-oriented programming, a class serves as a blueprint for creating objects. It defines the properties and behaviors that an object of that class will have. Therefore, a class in BO represents a group or collection of objects that share similar characteristics and functionalities.Rate this question:
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- 48.
Identify the applications from the BO below
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Integrate applications
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Data feeds
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Databases
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All of above
Correct Answer
A. All of aboveExplanation
The correct answer is "All of above" because the question asks to identify the applications from the given options, and all the options mentioned (Integrate applications, Data feeds, Databases) can be considered as applications in the context of the question. Therefore, the answer is all of the above.Rate this question:
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- 49.
What is slice and dice also known as?
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Structuring
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Positioning
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Rotation
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Cutting
Correct Answer
A. RotationExplanation
Slice and dice refers to the process of dividing data into smaller parts or subsets. However, in the context of the given question, slice and dice is being used as a term synonymous with rotation. Rotation involves changing the orientation or position of an object or data. Therefore, in this case, slice and dice is another way of referring to rotation.Rate this question:
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Aug 28, 2023Quiz Edited by
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Nov 14, 2015Quiz Created by
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