Data Warehousing and Data Mining MCQ Quiz - Objective Question with Answer for Data Warehousing and Data Mining - Download Free PDF

The correct answer is option 4.

Object Oriented DBMS:

Object-oriented DBMS is based on the object-oriented programming paradigm's model. They are useful for expressing both consistent data saved in databases and transitory data encountered in running applications. They employ simple, reusable parts known as objects.

Explanation:

Object-oriented databases closely relate to object-oriented programming concepts. The four main ideas of object-oriented programming are:

• Polymorphism
• Inheritance
• Encapsulation
• Abstraction

These four attributes describe the critical characteristics of object-oriented management systems.

Hence the correct answer is ​OODBMS.

Additional InformationNetwork DBMS:

Network DBMS is one where the relationships among data in the database are of type many to many in the form of a network.

Relational DBMS:

In relational databases, the database is represented in the form of relations. Each relation models an entity and is represented as a table of values. In the relation or table, a row is called a tuple and denotes a single record.

Distributed DBMS:

A distributed database is a set of interconnected databases that are distributed over the computer network or internet.

The correct answer is Fact table.

Key Points

• A Fact table contains the primary information in a data warehouse.
  • The fact table stores quantitative data for analysis and is often denormalized.
  • It usually contains foreign keys to dimension tables which describe the context of the facts.
  • The fact table is central to the star schema and snowflake schema of data warehousing.
  • It typically includes measures (numeric values) and keys to associated dimension tables.
  • Examples of measures in a fact table include sales amount, quantity sold, and transaction count.

Additional Information

• Fact tables are optimized for query performance and are often indexed to speed up data retrieval.
• They are typically large in size and grow quickly as more transactional data is added over time.
• Fact tables can be categorized into different types such as transactional, periodic snapshot, and accumulating snapshot fact tables.
• Unlike dimension tables, which store descriptive attributes, fact tables focus on storing measurable, quantitative data.

The correct answer is It supports strategic decision-making by providing a unified view of data from various sources.

Key PointsThe passage indicates that by offering a consolidated view of data from multiple sources, Data Warehouses enhance an organization's capability for strategic decision-making and adaptability to market changes.

The correct answer is A star schema has a central fact table connected to dimension tables, while a snowflake schema normalizes dimension tables into related tables.

Key PointsThe passage outlines the structure of both schemas, specifically noting that the star schema features a central fact table linked to dimension tables, whereas the snowflake schema normalizes those dimensions into multiple related tables.

The correct answer is Star and snowflake schemas

Key PointsThe passage discusses both the star and snowflake schemas as methods for organizing data within a data warehouse, highlighting their importance for efficient querying and reporting.

Data cleaning:

• Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated and these data is usually not helpful when it comes to analyzing data because it may hinder the process or provide inaccurate result.

Data extraction:

• Data extraction is the process of obtaining data from a database.
• Data extraction is the first step in a data ingestion process called ETL.

Data aggregate:

• Data aggregation is a process where data is gathered and expressed in a summary form.

Data compression:

• Data compression is the process of modifying, encoding or converting the bits structure of data in such a way that it consumes less space on disk.

Therefore option 4 is the correct answer.

Retail analytics focuses on providing insights related to sales, inventory, customers, and other important aspects crucial for a merchant's decision-making process. It is used to help make better choices, run a business more efficiently, and deliver improved customer service analytics.

Data mining:

• Data mining is a concept of computer science but has played a significant role in the retail industry as it helps retailers to learn the buying behavior and patterns of their customers.
• Retailers keep on collecting information about seasonal product sales, transactional data, and demographics, etc. the collected data is of no use if it is not converted into useful knowledge and converting data into knowledge requires a proper mechanism.
• Data mining is proved to be one of the most important tools to identify useful information from the large pool of information collected over time.
• It is also used to improve revenue generation and reduce the costs of business.

Therefore, retail analytic applications involve the use of Data mining techniques to gain insights into customer's buying patterns.

Factor analysis:

• Factor analysis is a technique used to reduce a large number of variables into a few factors.
• This technique extracts maximum common variance from all variables and puts them into a common score.
• As an index of all variables, that score can be used for further analysis.

Data scraping:

• Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program.
• It often ignores binary data, display formatting, redundant labels, and other information that is either irrelevant or hinders automated processing.
• It is generally considered as ad-hoc, inelegant techniques used as a last resort when no other mechanism for data interchange is available.

Data cloning:

• Data cloning refers to a complete and separate copy of a database system that includes the business data, the DBMS software, and any other application that makes up the environment.
• Data cloning involves both fully functional and separate in its own right.
• The cloned data can be modified at its inception due to configuration changes or data subsetting.

Regression analysis:

• Regression analysis is a reliable method of identifying which variables have an impact on a topic of interest.
• The process of performing a regression allows you to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other.

Enterprise Resource Planning (ERP):

• Type of software that organizations use to manage day to day business activities such as accounting, project management and supply chain operations.
• Helps plan budget, predict and report on an organization’s financial results.
• It is an example of multiuser database application

Hence option 2 is the correct answer.

The correct answer is Data mining.

Key Points

• Data mining - Data mining is the process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis.
• It helps data scientists easily analyze enormous amounts of data quickly.

Additional Information

• Data scientists can use the information to detect fraud, build risk models, and improve product safety.
• It helps data scientists quickly initiate automated predictions of behaviors and trends and discover hidden patterns.

• Data collection - It is the process of gathering data for use in business decision-making, strategic planning, research, and other purposes.
• Data may be grouped into four main types based on methods for collection:

1. Observational.
2. Experimental.
3. Simulation.
4. Derived.

• Data sampling -It is a statistical analysis technique used to select, process, and analyze a representative subset of a population.
• Types of sampling: Random, Systematic, Convenience, Cluster, and Stratified.

Concept:

Data warehouse is the process of collecting aand managing the data from different sources and use them for a business purpose. It is more related to query processing rather than transaction processing.

Explanation:

Data warehouse usually contains historical data concluded from transactional data. A data warehouse includes ETL(extraction, transformation, and loading) , OLAP engine, other tools that manages the collection of data and deliver this data to business users.

Staging area in the data warehouse includes this ETL which makes the data useful for further processing. Staging area is present between data source and destination which is mostly data marts.

Architecture of data warehouse:

Data warehousing:

It is a phenomenon that grew from huge amount of electronic data stored in recent years and from the urgent need to use that data to accomplish goals that go beyond the routine tasks linked to daily processing. Data warehousing is used in various applications such as: Trade, craftsmanship, financial services, transport industry, telecommunication etc.

Data warehouse architecture contains following points:

• Source system
• Source data transport layer
• Data quality control and profiling layer
• Metadata management layer
• Data integration layer
• Data processing layer
• End user reporting layer

Developing a data warehouse is no different from any other project. It requires complete planning, requirements, design, implementation. Data warehouse contains summary data that is not found in operational environment. Developing strategy of data warehouse is to establish virtual data warehouse. It is simply to build a copy of operational data from a single operational system and enable the data warehouse from a series of information access tools.

Diagram:

The correct answer is (A), (C) and (D) only

Key PointsData Mining:

• Data mining is a process used to find patterns and relationships in huge data sets that may be used to assist solve business challenges. 
• Enterprises can forecast future trends and make more educated business decisions thanks to data mining techniques and technologies.
• Data mining is a crucial component of data analytics as a whole and one of the fundamental fields in data science, which makes use of cutting-edge analytics methods to unearth valuable information in data sets.

Important Points From the given option, the following statements are true regarding data mining:

• Predicting future trends based on information available
• Analysing demographic information about customers
• Credit risk analysis

Concept:

Data warehouse is the process of collecting aand managing the data from different sources and use them for a business purpose. It is more related to query processing rather than transaction processing.

Explanation:

Data warehouse usually contains historical data concluded from transactional data. A data warehouse includes ETL(extraction, transformation, and loading) , OLAP engine, other tools that manages the collection of data and deliver this data to business users. A data warehouse is an example of OLAP system. 

OLAP (Online analytical processing) system helps in analyzing the data. It allow analysts to efficiently answer questions that require data from multiple source systems.

Diagram : 

The correct  answer is option 4.

Solution :

In the warehouse , No. of aisles = 50

Horizontal locations in each aisle = 85

No. of shelves =5

Therefore, total number of bins =  50 *  85 * 5 = 21250

According to the pigeonhole principle, if there are N+1 pigeon then there must be N pigeonholes, such that at least  two pigeons are in same pigeonholes.

Here, N = 21250,therefore number of products = 21250+1 =21251 so that at least two products are in the same bin.