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

Last updated on Jun 12, 2025

For anyone interested in learning more about data management and analysis, Data Warehousing and Data Mining MCQs offer a simple yet effective learning route. These MCQs cover key aspects such as the process of data warehousing, various data mining techniques, and their real-world applications. Regular interaction with Data Warehousing and Data Mining MCQs will help deepen your understanding of these important topics in data science.

Latest Data Warehousing and Data Mining MCQ Objective Questions

Data Warehousing and Data Mining Question 1:

Big data is generally characterized by the three V's. What re the three V's?

  1. Volume, Viscosity, variety
  2. Variety, Velocity, Vivid
  3. Viscosity, Volume, Velocity
  4. Volume, Variety, Velocity
  5. None of the above

Answer (Detailed Solution Below)

Option 4 : Volume, Variety, Velocity

Data Warehousing and Data Mining Question 1 Detailed Solution

The correct answer is Volume, Variety, Velocity.

Key Points

  •  Big data refers to large and complex data sets that cannot be easily processed or managed using traditional data processing tools or database management systems.
  • It often involves large amounts of unstructured or semi-structured data, such as text, images, and video, that require advanced analytical techniques to extract meaningful insights.
  • The three V's of big data are:
  • Volume: The amount of data being generated is enormous and constantly growing. This can range from terabytes to petabytes of data.
  • Velocity: The speed at which data is being generated, processed, and analyzed is increasing. Real-time data streams are becoming more common, and the need for fast data processing and analysis is essential.
  • Variety: Data comes in many different formats, including structured, semi-structured, and unstructured data. This includes data from traditional sources such as databases and spreadsheets, as well as data from social media, weblogs, sensors, and other sources.

Hence, the correct answer is Volume, Variety, Velocity

Data Warehousing and Data Mining Question 2:

Which databases supports Polymorphism, Inheritance, Encapsulation, and Abstraction

concepts?

  1. RDBMS
  2. Distributed DBMS
  3. Network DBMS
  4. OODBMS
  5. None of the above

Answer (Detailed Solution Below)

Option 4 : OODBMS

Data Warehousing and Data Mining Question 2 Detailed Solution

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.

Data Warehousing and Data Mining Question 3:

Which of the following table contains the primary information in the data warehouse?

  1. Dimension table
  2. Fact table
  3. Lookup table
  4. Primary table

Answer (Detailed Solution Below)

Option 2 : Fact table

Data Warehousing and Data Mining Question 3 Detailed Solution

Data Warehousing and Data Mining Question 4:

Comprehension:

Read the below passage and answer the questions.

A Data Warehouse (DW) is a centralized repository that stores large volumes of structured and semi-structured data collected from various sources within an organization. It is designed to facilitate reporting and analysis, providing a foundation for business intelligence (BI) activities. Data warehousing allows organizations to consolidate their data from multiple systems, enabling them to generate insights and make informed decisions based on comprehensive data analysis.

The architecture of a data warehouse typically consists of three main components: data sources, the data warehouse itself, and the front-end tools used for querying and reporting. Data is extracted from various operational systems, transformed to ensure consistency and quality, and then loaded into the data warehouse in a process known as ETL (Extract, Transform, Load). This transformation process is crucial for maintaining data integrity and enabling efficient analysis.

Data warehouses often utilize a star or snowflake schema to organize data, allowing for efficient querying and retrieval of information. The star schema features a central fact table connected to multiple dimension tables, whereas the snowflake schema normalizes the dimension tables into multiple related tables. Both designs facilitate complex queries and reporting capabilities, making it easier for users to analyze trends, patterns, and key performance indicators (KPIs).

By providing a unified view of data from various sources, data warehouses support strategic decision-making and enhance an organization's ability to respond quickly to changing market conditions.

What benefit does a Data Warehouse provide to organizations as described in the passage?

  1. It stores data only from current operations.
  2. It replaces the need for operational databases.
  3. It focuses solely on unstructured data analysis.
  4. It supports strategic decision-making by providing a unified view of data from various sources.

Answer (Detailed Solution Below)

Option 4 : It supports strategic decision-making by providing a unified view of data from various sources.

Data Warehousing and Data Mining Question 4 Detailed Solution

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.

Data Warehousing and Data Mining Question 5:

Comprehension:

Read the below passage and answer the questions.

A Data Warehouse (DW) is a centralized repository that stores large volumes of structured and semi-structured data collected from various sources within an organization. It is designed to facilitate reporting and analysis, providing a foundation for business intelligence (BI) activities. Data warehousing allows organizations to consolidate their data from multiple systems, enabling them to generate insights and make informed decisions based on comprehensive data analysis.

The architecture of a data warehouse typically consists of three main components: data sources, the data warehouse itself, and the front-end tools used for querying and reporting. Data is extracted from various operational systems, transformed to ensure consistency and quality, and then loaded into the data warehouse in a process known as ETL (Extract, Transform, Load). This transformation process is crucial for maintaining data integrity and enabling efficient analysis.

Data warehouses often utilize a star or snowflake schema to organize data, allowing for efficient querying and retrieval of information. The star schema features a central fact table connected to multiple dimension tables, whereas the snowflake schema normalizes the dimension tables into multiple related tables. Both designs facilitate complex queries and reporting capabilities, making it easier for users to analyze trends, patterns, and key performance indicators (KPIs).

By providing a unified view of data from various sources, data warehouses support strategic decision-making and enhance an organization's ability to respond quickly to changing market conditions.

How does a star schema differ from a snowflake schema in data warehousing?

  1. A star schema features multiple fact tables, while a snowflake schema has a single fact table.
  2. A star schema has a central fact table connected to dimension tables, while a snowflake schema normalizes dimension tables into related tables.
  3. A star schema allows for faster data retrieval, while a snowflake schema is slower.
  4. A star schema is only used for historical data, while a snowflake schema is for real-time data.

Answer (Detailed Solution Below)

Option 2 : A star schema has a central fact table connected to dimension tables, while a snowflake schema normalizes dimension tables into related tables.

Data Warehousing and Data Mining Question 5 Detailed Solution

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.

Top Data Warehousing and Data Mining MCQ Objective Questions

The process of removing deficiencies and loopholes in the data is called as____

  1. Data aggregation
  2. Extraction of data
  3. Compression of data
  4. Cleaning of data

Answer (Detailed Solution Below)

Option 4 : Cleaning of data

Data Warehousing and Data Mining Question 6 Detailed Solution

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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.

Which of the following retail analytic applications involve(s) the use of search techniques to gain insights into customer's buying patterns?

A. Factor analysis

B. Regression analysis

C. Data mining

D. Data scrapping

E. Data cloning

Choose the most appropriate answer from the options given below:

  1. A and B only
  2. B and C only
  3. A, C and D only
  4. C only

Answer (Detailed Solution Below)

Option 4 : C only

Data Warehousing and Data Mining Question 7 Detailed Solution

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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.

An Enterprise Resource Planning application is an example of a(n) ______.

  1. Single-user database application
  2. Multiuser database application
  3. E-commerce database application
  4. Data mining database application

Answer (Detailed Solution Below)

Option 2 : Multiuser database application

Data Warehousing and Data Mining Question 8 Detailed Solution

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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.

________ is referred to as discovering patterns based on a large set of previous year's data.

  1. Data validation
  2. Data collection
  3. Data mining
  4. Data sampling

Answer (Detailed Solution Below)

Option 3 : Data mining

Data Warehousing and Data Mining Question 9 Detailed Solution

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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.

 _________ is an intermediate storage area used for data processing during the extract, transform and load process of data warehousing .  

  1. Buffer
  2. Virtual memory 
  3. Staging area
  4. inter-storage area 

Answer (Detailed Solution Below)

Option 3 : Staging area

Data Warehousing and Data Mining Question 10 Detailed Solution

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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 warehouse contains ______ data that is never found in operational environment.

  1. Scripted
  2. Encoded
  3. Encrypted
  4. Summary

Answer (Detailed Solution Below)

Option 4 : Summary

Data Warehousing and Data Mining Question 11 Detailed Solution

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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:

Which of the following statements is/are correct regarding application of data mining techniques?

(A) Predicting future trends based on information available

(B) Electronic data interchange

(C) Analysing demographic information about customers

(D) Credit risk analysis

Choose the correct answer from the options given below:

  1. (B) and (D) only
  2. (A), (B), (C) and (D) only
  3. (A), (B) and (D) only
  4. (A), (C) and (D) only

Answer (Detailed Solution Below)

Option 4 : (A), (C) and (D) only

Data Warehousing and Data Mining Question 12 Detailed Solution

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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

What do data warehouses support?  

  1. OLAP
  2. OLAP AND OLTP 
  3. OLTP
  4. Operational database 

Answer (Detailed Solution Below)

Option 1 : OLAP

Data Warehousing and Data Mining Question 13 Detailed Solution

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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 : 

A company stores products in a warehouse. Storage bins in this warehouse are specified by their aisle, location in the aisle, and self. There are 50 aisles, 85 horizontal locations in each aisle, and 5 shelves throughout the warehouse. What is the least number of products the company can have so that at least two products must be stored in the same bin?

  1. 251
  2. 426
  3. 4251
  4. 21251

Answer (Detailed Solution Below)

Option 4 : 21251

Data Warehousing and Data Mining Question 14 Detailed Solution

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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.

A central repository of information that can be analyzed to make more informed decisions is known as:

  1. Data mining
  2. Data storing
  3. Data Sorting
  4. Data warehouse

Answer (Detailed Solution Below)

Option 4 : Data warehouse

Data Warehousing and Data Mining Question 15 Detailed Solution

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The correct answer is Data warehouse

Key Points

  • A Data warehouse is a central repository of information that can be analyzed to make more informed decisions.
  • It is designed to store large volumes of data that can be queried and analyzed for business intelligence purposes.
  • Data warehouses integrate data from multiple sources, ensuring that the data is consistent and reliable for analysis.
  • They are optimized for read-heavy operations and complex queries, unlike operational databases that are optimized for transaction processing.
  • Typical operations in a data warehouse include data cleaning, data transformation, and data loading.

Additional Information

  • Data mining is a process used to discover patterns and relationships in large datasets.
  • Data storing refers to the act of saving data in a storage medium for future use.
  • Data sorting involves arranging data in a specific order or sequence.
  • The concept of a data warehouse was introduced by Bill Inmon in the 1990s, who is often considered the father of data warehousing.
  • Data warehouses support OLAP (Online Analytical Processing) which allows users to analyze data from multiple database systems at once.

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