Dmw-quiz-1-a

The operational environment is the correct answer because it refers to the system where the data warehouse collects its data from. The operational environment is where the day-to-day operations of an organization take place, and it is the primary source of data for the data warehouse. This environment includes various systems, databases, and applications that capture and store data generated by the organization's operational processes. Therefore, the operational environment is the starting point for data collection in a data warehouse.

Operational metadata refers to the information that defines the structure of data stored in operational databases. It provides details about the organization, format, and relationships of the data, allowing for efficient storage, retrieval, and manipulation of the data. This type of metadata is essential for the proper functioning of operational databases and ensuring data integrity and consistency. User-level metadata, transaction data, and data mining metadata do not specifically pertain to the structure of data in operational databases.

Concept Hierarchy is the correct answer because it refers to the sequencing of mappings from low-level concepts to higher-level concepts. This means organizing and structuring data in a hierarchical manner, where more general concepts are at the top and more specific concepts are at the bottom. It helps in understanding the relationships and dependencies between different concepts, allowing for easier analysis and navigation of data.

Data Cleaning is the process of identifying and correcting errors, inconsistencies, and inaccuracies in data. It involves various techniques and methods to detect and rectify these errors, such as removing duplicate entries, handling missing values, standardizing data formats, and resolving inconsistencies. The goal of data cleaning is to improve data quality and ensure that the data is accurate, complete, and reliable for analysis and decision-making purposes.

The given equation, mean - mode = 3(mean - median), indicates that the difference between the mean and the mode is three times the difference between the mean and the median. This relationship suggests that the mode is closer to the mean than the median is. In other words, the mode is more representative of the central tendency of the data compared to the median.

Bitmap indexing is used in OLAP cube for quick searching. It is a data structure that uses bitmaps to represent the presence or absence of rows in a table. This indexing technique is particularly useful for low cardinality data, where there are a limited number of distinct values in a column. By using bitmaps, it allows for efficient filtering and aggregation operations in OLAP cubes, enabling faster query processing and analysis. Joint bitmap, composite join indices, and none of the above are not relevant to the given statement.

OLAP stands for Online Analytical Processing, which is a technology used for analyzing and processing large amounts of data. It involves informational processing, as it focuses on extracting valuable information from data. It also involves lots of scans, as it scans through the data to find patterns and relationships. Additionally, OLAP is known for its ability to summarize and consolidate data, providing a high-level view of the information. Therefore, the correct answer is "All of the above" as all the given options accurately describe OLAP.

A data warehouse typically consists of three tiers: the bottom tier is the data storage layer, where the actual data is stored; the middle tier is the data integration layer, where data from various sources is collected, transformed, and loaded into the warehouse; and the top tier is the data access layer, where users can query and analyze the data. This three-tier architecture allows for efficient data storage, integration, and retrieval, making it a popular choice for data warehousing implementations.

When data is extracted using application program interfaces (APIs), the devices that facilitate the communication between different networks or systems are known as gateways. Gateways act as intermediaries, translating data from one format to another to enable seamless communication and integration between different systems. They can be used to connect networks with different protocols or to enable communication between different software applications. Therefore, gateways are the correct answer in this context.

The star schema is composed of one fact table. In a star schema, the fact table contains the measurable data or the numerical values that are being analyzed or reported on. It is surrounded by multiple dimension tables, which provide context and additional details about the data in the fact table. The fact table is at the center of the star schema and is connected to the dimension tables through foreign key relationships. This structure allows for efficient and simplified querying and analysis of data.

The correct answer is SLICE. In the context of data analysis and multidimensional cubes, slicing refers to the process of selecting a specific portion or subset of data along one dimension. This allows analysts to focus on a specific aspect or category of the data, while disregarding the other dimensions. Therefore, slicing performs selection on one dimension of the given cube.

A data cube is a multidimensional representation of data that allows for efficient analysis and querying. It is used to model data that has multiple dimensions, such as time, geography, and product. A data cube organizes data into a structure that enables users to easily slice, dice, and drill down into the data to gain insights and make informed decisions. It is commonly used in data warehousing and business intelligence applications to support complex analytical queries.

A data warehouse contains summary data that is not typically found in the operational environment. This means that the data in the warehouse is aggregated, organized, and presented in a way that allows for easy analysis and reporting. The purpose of a data warehouse is to provide a consolidated view of data from different sources, making it easier for users to make informed decisions based on the summarized information. In contrast, operational environments contain detailed and transactional data that is used for day-to-day operations.

ROLAP stands for Relational Online Analytical Processing. It is a type of OLAP (Online Analytical Processing) that stores and manages warehouse data using a relational database management system. ROLAP allows users to access and analyze data from multiple dimensions and hierarchies, providing a flexible and scalable solution for data analysis. Unlike MOLAP and HOLAP, ROLAP does not require pre-aggregation of data and can directly query the relational database. SQL Servers, on the other hand, are a type of database management system that can be used to implement ROLAP or other OLAP solutions.

The model is kept in the normalized form in a Snow Flake Schema. In a Snow Flake Schema, the dimension tables are further normalized by splitting them into multiple tables. This helps in reducing data redundancy and improving data integrity. The normalized form allows for efficient data storage and retrieval, making it easier to maintain and update the model.