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

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:

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)

Data Warehousing and Data Mining Question 1 Detailed Solution

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.

Data Warehousing and Data Mining Question 2:

Which of the following techniques allow extracting valuable information from large volumes of data and predicting outcomes of future situations?

1. Data warehousing
2. Data mining
3. Data analysis
4. Database management
5. Data managing

Answer (Detailed Solution Below)

Data Warehousing and Data Mining Question 2 Detailed Solution

Data mining techniques allow extracting valuable information from large volumes of data and predicting outcomes of future situations

Data Mining: Bringing out the most important data from a huge data set is known as data mining. With the help of Artificial Intelligence AI , a CRM tries to answer the most important data queries of a business. In a CRM, data mining helps to find relations among data, classifies the customers according to business value, and helps to find answers to the following questions −

• Which customers the business should target?
• What is the cost of customer acquisition?
• Which customers are buying or not buying the products?
• Who are high/medium/low margin customers?
• What profile customers are defaulting payment repeatedly?
• How can a business segment it's market?
• Can the business offer a common price for all customer segments?

For example , an analyst at Flipkart found that customers who buy a smartphone are also looking for tempered glass and cover for it. He used this information to create a bundle in which there is a smartphone, a tempered glass suited to the screen size of the smartphone, and a cover. This helped in accelerating the sale of the tempered glass and the phone back cover.

Data Warehousing and Data Mining Question 3:

Which one of the following is the reason of using aggregate in dimensional model of data warehousing?

1. it helps in retrieving data from operational system
2. it takes the dimension and change its granularity
3. it stores data in one operational system
4. All of these
5. None of the above

Answer (Detailed Solution Below)

Data Warehousing and Data Mining Question 3 Detailed Solution

Aggregations are the summarization of fact related data for the purpose of improved performance. These are the confirmed fact tables since they provide the same query result as the detailed fact table.

• These are used when number of records to be processed are large.
• There are used to take the dimension and changes its granularity.
• These are also known as pre calculated summary data since these are pre- computed.
• When changing the granularity of the dimension, fact table should be partially summarized to fit the new dimensions.

Data Warehousing and Data Mining Question 4:

1. Same as database
2. Same as data warehouse
3. Market survey
4. Analyzing data on customers
5. None of the above

Answer (Detailed Solution Below)

Data Warehousing and Data Mining Question 4 Detailed Solution

Customer Relationship Management (CRM) is a process of managing a company's interactions and relationships with customers and potential customers.

Data mining:

• Data mining is the process of finding different patterns and relationships within a large volume of data.
• Data mining uses various algorithms and techniques to find trends from the existing data warehouses.
• In CRM, data mining means analyzing data on customers and assisting the business in finding and evaluating relevant information.
• This technique can be used to get a clear picture of the customer's life cycle.
• The customer life-cycle includes customer identification, acquisition, retention, and development.
• Data mining usually involves the use of predictive, forecasting, and descriptive modeling techniques as its key elements.
• The more data available in the database, the more accurate the models will be, and more value will be gained as well.

• CRM with the help of data analysis enables an organization to engage in many useful activities.
• Data mining along with data analysis aids in managing customer retention, choosing the right segments, setting optimal pricing policies, and ranking suppliers as per need.
• Data mining with the help of Artificial Intelligence will be able to shape the future of CRM and aid companies to become more customer-oriented.
• The combination of CRM and data mining tools will increase the knowledge and understanding of customers, products, and transactional data, improving tactical marketing activity and strategic decision-making.

Therefore, i n CRM, data mining means analyzing data on customers.

Data Warehousing and Data Mining Question 5:

Which of the following relational schemas and other metadata about relations are stored in a structure?

1. Data-Dictionary Storage
2. Tertiary Storage
3. Flash Storage
4. Physical Storage
5. Data Storage

Answer (Detailed Solution Below)

Data Warehousing and Data Mining Question 5 Detailed Solution

The correct answer is option 1.

Concept:

Data-Dictionary Storage​:

In the data storage method, Relational schemas and other metadata about relations are stored in a structure called the data dictionary or system catalog.

A data dictionary is similar to the A-Z dictionary of a relational database system, storing all information for each relation in the database.

The types of information a system must store are:

• Name of the relations
• Name of the attributes of each relation
• Lengths and domains of attributes
• Name and definitions of the views defined on the database
• Various integrity constraints

Hence the correct answer is Data-Dictionary Storage.

Additional Information Tertiary Storage:

In a large database system, some of the data may have to reside in tertiary storage. The two most common tertiary storage media are optical disks and magnetic tapes.

Flash memory is an electronic non-volatile computer memory storage medium that can be electrically erased and reprogrammed.

Physical storage includes devices like external hard drives, SD cards, and flash drives.

Top Data Warehousing and Data Mining MCQ Objective Questions

Data Warehousing and Data Mining Question 6

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

Answer (Detailed Solution Below)

Data Warehousing and Data Mining Question 6 Detailed Solution

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.

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Data Warehousing and Data Mining Question 7

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)

Data Warehousing and Data Mining Question 7 Detailed Solution

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 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.

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Data Warehousing and Data Mining Question 8

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

Answer (Detailed Solution Below)

Data Warehousing and Data Mining Question 8 Detailed Solution

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.

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Data Warehousing and Data Mining Question 9

_________ 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)

Data Warehousing and Data Mining Question 9 Detailed Solution

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:

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Data Warehousing and Data Mining Question 10

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

Answer (Detailed Solution Below)

Data Warehousing and Data Mining Question 10 Detailed Solution

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:

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Data Warehousing and Data Mining Question 11

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)

Data Warehousing and Data Mining Question 11 Detailed Solution

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

Key Points Data 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

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Data Warehousing and Data Mining Question 12

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

Answer (Detailed Solution Below)

Data Warehousing and Data Mining Question 12 Detailed Solution

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.

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.

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Data Warehousing and Data Mining Question 13

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?

Answer (Detailed Solution Below)

Data Warehousing and Data Mining Question 13 Detailed Solution

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.

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Data Warehousing and Data Mining Question 14

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

Answer (Detailed Solution Below)

Data Warehousing and Data Mining Question 14 Detailed Solution

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.

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Data Warehousing and Data Mining Question 15

What does the following statement represent in data migration? "Locate the useful information required to populate the customer relationship management and identify who maintains it, what it contains and how accurate it is."

1. Identify available migration tools
2. Test before migrating
3. Improve the data
4. Find the data

Answer (Detailed Solution Below)

Data Warehousing and Data Mining Question 15 Detailed Solution

The correct answer is to Find the data.

Key Points

Data Migration:

Data migration is one of the recent developments in technology. It is a process where the firms employ technology to move data from one place to another, or from application to another, or from one format to another. Data is the most crucial aspect for any organization and many a time firms need to move this data. Data migration comes to the rescue in such a situation. While most of the information today is stored on cloud-based applications, data migration is also done through these applications. Data Migration is generally done in three steps- finding the data, transforming data, and loading the data. Data migration can be of the following types:

• Storage Migration- It is a process where the data is moved from the existing system to a new system by providing access to the data. It uses the cloning method of data migration.
• Cloud Migration- It is a process of moving the data from a data cloud center or from a data premises to another cloud storage system.
• Application Migration- It is a process of moving the data from one environment to another environment. It includes movement from the centre of the premises to a cloud or from one cloud to another.