In the ever-evolving landscape of data-driven decision-making, mastering the fundamentals of Data Mining is paramount. To aid your journey, we present a comprehensive collection of the top 60 Data Mining MCQ Questions and Answers in a convenient PDF format for 2024.
These meticulously crafted questions encompass a wide range of topics, from basic concepts to advanced techniques, providing a valuable resource for students, professionals, and enthusiasts alike. Whether you’re delving into the realm of Data Mining or seeking to enhance your expertise, this curated compilation promises to sharpen your skills and deepen your understanding of this dynamic field. Lets brief the few terms before jumpup.
What is the data mining definition?
Data mining involves the extraction of patterns, relationships, and insights from extensive datasets through the use of diverse methodologies such as statistics, machine learning, and artificial intelligence.
What is the data miner definition?
A data miner is an individual or software system that uses data mining techniques to extract meaningful information from large datasets.
What is the application of data mining?
Data mining finds applications in various fields, including business, healthcare, finance, marketing, and scientific research. It helps uncover patterns, trends, and insights within large datasets to make informed decisions.
What are the data mining benefits?
Data mining offers benefits such as improved decision-making, enhanced customer insights, better market understanding, increased operational efficiency, and competitive advantage.
Data Warehouse Meaning:
“data warehouse” refers to a centralized repository that stores structured and organized data for analysis, reporting, and decision-making purposes. It offers a consolidated view of an organization’s data landscape.
Data Warehousing Definition:
Data warehousing involves collecting, storing, and managing data from various sources in a central repository. It aims to provide a unified and consistent data view for analysis and reporting.
Data Warehousing in Data Mining:
Data warehousing in data mining involves the integration of data warehousing principles with data mining techniques. This synergy enables organizations to leverage historical data to make predictions and informed decisions.
Database Warehouses:
Database warehouses, commonly known as data warehouses, are specialized databases optimized for analytical tasks. They store and manage large volumes of data for efficient querying and reporting.
Dataware:
Dataware refers to the software infrastructure and tools used for data warehousing. It encompasses data extraction, transformation, loading (ETL), storage, and retrieval processes.
What is Oracle Data Warehouse:
Oracle Data Warehouse provides organizations with an efficient means to manage and analyze vast amounts of data. It provides advanced analytics and reporting capabilities.
Tools for Data Warehousing in Data Mining:
Various tools and technologies support data warehousing in data mining, including ETL tools, data integration platforms, and analytics software. These tools streamline the process of data preparation and analysis.
Top 60 Data Mining MCQ Questions and Answers
1. Data Warehouse is defined as subject-oriented, integrated, time-variant and ___.
a. Volatile
b. Distributed
c. Non-Volatile
d. None of the above
Ans: c
2. Which one of the following is not a tool for Data warehouse development?
a. COGNOS
b. SCCS
c. Informatica
d. Business Objects
Ans: b
3. The Data Warehouse does not cater to the Real-time operational requirements of the enterprise. (True/False).
Ans: True
4. Data Warehouse contains data for ___ purpose.
a. Real-Time Operation
b. Analysis
c. Validation
d. All of the above
Ans: b
5. In Data Warehouse, the requirements are gathered subject area wise. (True/False)
Ans: True
6. Which of the following is a Source Data Component in Data Warehouse?
a. Production Data
b. Sales Data
c. Marketing Data
d. Purchase Data
Ans: a
7. Data Marts are.
a. Department level
b. Limited in size
c. Read-only
d. All the above
Ans: d
8. The three major Data Staging Components are Data Extraction, Data Transformation and ___.
a. Data Retrieval
b. Data Loading
c. Data Refresh
d. Data Access
Ans: d
9. Dimensional model can be implemented with the following databases.
a. Relation database
b. MDDB
c. Flat files
d. Excel data files
Ans: a
10. Fact-Tables usually consists of ___ relationships.
a. Many to many
b. One too many
c. One to one
d. Many to one
Ans: a
11. Each Dimension table has a ___ relationship to the fact table.
a. Many to many
b. One too many
c. Many to one
d. One to one
Ans: b
12. Dimensional table and a fact table can be connected with the following database keys:
a. Foreign key
b. Surrogate key
c. Candidate key
d. All of the above
Ans: a
13. In Data Warehouse, a single record link to all the duplicate record in the source systems is called ___.
a. Decoding of fields
b. De-duplication
c. Merging of Information
d. Summarization
Ans: b
14. Which of the following is not a type of data loading?
a. Initial load
b. Incremental load
c. Iterative load
d. Full refresh
Ans: c
15. Adding value to the data to give it more meaning is called ___.
a. Data cleansing
b. Data profiling
c. Data integration
d. Data Enrichment
Ans: d
16. How many levels does CMM have?
a. One
b. Four
c. Five
d. Two
Ans: c
17. The full form of CMM is:
a. Capability Maturity Model
b. Capability Model Maturity
c. Comprehensive Material Management
d. Computer Material Management
Ans: a
18. OLAP stands for.
a. On-Line Application Processing
b. On-Line Analytical Processing
c. On-Line Ability Processing
d. None of the above
Ans: b
19. Which of the following are the intermediate servers that stand in between a relational back-end server and client front end tools?
a. ROLAP
b. MOLAP
c. HOLAP
d. All of the above
Ans: d
20. A dimensional table does not contain hierarchies. (True/False)
Ans: True
21. ___ is used as a (dynamic) indexing method in relational database management systems.
a. Bit map indexing
b. B+ tree indexing
c. Compression indexing
d. Clustered indexing
Ans: b
22. Parallelism improve processing for.
a. Large table scans and joins
b. Creation of large indexes
c. Bulk inserts, update and deletes
d. All of the above
Ans: d
23. According to Ralph Kimball, Back-room metadata guides.
a. Extraction
b. Cleaning
c. Loading processes
d. All the above
Ans: d
24. Storing, data mapping and transformation from source systems to the Data Warehouse fall into:
a. Technical metadata
b. Operational metadata
c. Business metadata
d. None of the above
Ans: a
25. Key hierarchies and key performance indicators are ___ kind of metadata.
a. Technical metadata
b. Operational metadata
c. Business metadata
d. None of the above
Ans: c
26. Which of the following is the white box testing?
a. Unit testing
b. Regression
c. User accepting testing
d. Integration testing
Ans: a
27. Which of the following are the main areas of testing that should be done for the ETL process.
a. Making sure that all the records in the source system that should be brought into the warehouse and all the components of the ETL process are complete.
b. All of the extracted source data is correctly transformed into dimension tables and fact tables
c. All of the extracted and transformed data is successfully loaded into Data Warehouse
d. All of the above
Ans: d
28. The advantage of using a data cube is that it allows fast indexing to pre-computed summarized data. (True/False)
Ans: True
29. RAID stands for.
a. Rapid Application integration and Development
b. Redundant Array of Inexpensive Disks
c. Redundant Application of Inexpensive Disks
d. Redundant Array of Integrated Disks
Ans: b
30. Which of the following analytic tools should be used for extracting the data from the Data Warehouse?
a. OLAP tools
b. Data mining tools
c. SQL
d. All the above
Ans: d
31. Which of the following data mining technique is used for optimization?
a. Artificial Neural Networks
b. If then rule induction
c. Genetic algorithms
d. Decision trees
Ans: c
32. Which of the following tools provide enterprise intelligence?
a. Data mining
b. Data warehouse
c. Databases
d. None of the above
Ans: a
33. Predictive modelling requires which of the following Data set for initial model creation?
a. Training data set
b. Test data set
c. Raw data set
d. All of the above
Ans: a
34. Click stream data is used for the following.
a. To track the user activity on the web page
b. To study customer buying patterns
c. Feed about web site design
d. All the above
Ans: d
35. Which of the following is the private network to access the data through the web.
a. Internet
b. Extranet
c. Intranet
d. None of the above
Ans: c
36. Web-enabling the Data Warehouse uses the following as the information delivery mechanism.
a. Web technology
b. Grid computing
c. Artificial intelligence
d. None of these
Ans: a
37. Web house is what kind of network?
a. Distributed system
b. Client and server only
c. Parallel system
d. None of the above
Ans: a
38. The system delivers the result of requests for information through remote browsers is called.
a. Web browser
b. Information delivery
c. Data presentation
d. Data dissemination
Ans: b
39. Who is called the Father of Data Warehouse?
a. Charles Babbage
b. Ralph Kimball
c. Bill Inmon
d. Fritz Bauer
Ans: c
40. Which of the following schema supports the normalization in dimensional modelling.
a. Star schema
b. Snow-Flake schema
c. Fact-Constellation
d. None of these
Ans: b
41. CMMI means ___.
a. Capability Model Maturity Integration
b. Comprehensive Material Management Information
c. Capability Maturity Model Information
d. Capability Maturity Model Integration
Ans: d
42. Data Cubes contains ___ and ___.
a. Facts, Information
b. Dimensions, Weight
c. Dimensions, Facts
d. Data, Information
Ans: c
43. The hypercube is the cube with ___dimensions.
a. Three
b. Two
c. Four
d. One
Ans: c
44. Writing the same data to two disk drives connected to the same controller ifs known as ___.
a. Data Duplexing
b. Data Mirroring
c. Disk Striping
d. Data Profiling
Ans: b
45. ___ provides the Enterprise with intelligence and ___ provides the Enterprise with a memory.
a. Data Warehouse, Databases
b. Databases, Data Mining
c. Data mining, Data warehouse
d. Data Warehouse, Data Mining
Ans: c
46. Which of the following is an open-source Data mining tool?
a. Clementine
b. Intelligent Miner
c. Weka3
d. Enterprise Miner
Ans: c
47. In the star schema, the dimension table is ___ and the fact table is ___.
a. Wide, Wide
b. Wide, Deep
c. Deep, Wide
d. Deep, Deep
Ans: b
48. Which of the following is an open-source ETL tool?
a. Cover
b. SAS data Integrator
c. Cognos Decision Stream
d. Microsoft DTS
Ans: a
49. Confirmed dimension allows user to:
a. Share non-Key dimension data
b. Query Across fact tables with consistency
c. Work on fact and business subjects for which all users have the same meaning
d. All of the above
Ans: d
50. Which of the following is true for the CMM level2?
a. Data quality issues are acknowledged
b. Major problems are handled as and when they surfaced
c. Both a and b.
d. None of these
Ans: c
51. Data Warehouse is ___ triggered whereas OLTP is ___ triggered.
a. Event, User
b. System, User
c. System, Event
d. Insert, Update
Ans: b
52. UAT means.
a. User Acquisition Test
b. User Acceptance Test
c. Usage Acceptance Test
d. Usage Ambiguity Test
Ans: b
53. Meta Data means.
a. Data about Data
b. Catalogue of data
c. Data Warehouse Roadmap
d. All of the above
Ans: d
54. Which of the following interfaces are used to access the Data Warehouse?
a. Browser
b. Search engine
c. Active X applets
d. All the above
Ans: d
55. Data mining is ___ driven approach not ___ driven approach.
a. Event, Data
b. Data, User
c. User, Event
d. User, Data
Ans: b
56. Which of the following is true for Administrative Metadata?
a. Access rights, protocols, physical location, retention criteria
b. Protocols, audit controls, source tables, usage statistics
c. Access rights, audit control, process automation, usage statistics
d. Audit control, schema definition, physical location, retention criteria
Ans: a
57. Which of the following RAID level does not implement error checking?
a. RAID1
b. RAID (0+1)
c. RAID0
d. RAID5
Ans: c
58. ___ and ___ of data take place on a large scale in the data staging area.
a. Sorting, searching
b. Searching, merging
c. Sorting, merging
d. Searching, acquisition
Ans: c
59. True/False
1. Data Warehouse contains only aggregated data and individual transactions.
2. A dimension is an entity or Subject area, which can group the data.
3. E-R modelling and dimensional modelling are the same.
a. 1-T, 2-F, 3-T
b. 1-F, 2-F, 3-F
c. 1-T, 2-T, 3-F
d. 1-F, 2-T, 3-T
Ans: c
60. True/False
1. Sorting the data in the given source file is a transformation
2. OLAP tools enable the user to access the data in Data Warehouse in an interactive manner.
3. Data mining is a data-driven approach, not a user-driven approach
a. 1-T, 2-T, 3-T
b. 1-F, 2-F, 3-F
c. 1-T, 2-T, 3-F
d. 1-F, 2-T, 3-T
Ans: a
Relational Database Management System MCQ with Answer
Download Top 60 Data Mining MCQ Questions and Answers in Pdf
FAQs related to Data Mining and Data Warehousing
Q1: How Does Data Warehousing Enhance Data Analysis?
Answer: Data warehousing enhances data analysis by providing a structured and unified environment for storing and retrieving data. It enables organizations to perform complex queries, uncover patterns, and generate valuable insights.
Q2: What Are Some Common Data Mining Techniques Used in Data Warehousing?
Answer: Some standard data mining techniques used in data warehousing include clustering, classification, regression, association rule mining, and anomaly detection. These techniques help organizations discover hidden relationships within their data.
Q3: Can You Provide an Example of Data Warehousing in Action?
Answer: Certainly! Imagine a retail company that wants to analyze its sales data over the past five years to identify customer trends and preferences. Using a data warehouse, the company can consolidate data from various sources, run analytical queries, and generate reports guiding marketing and inventory decisions.
Q4: How Does Data Warehousing Benefit Business Intelligence Initiatives?
Answer: Data warehousing plays a crucial role in business intelligence by offering a reliable and consistent data source for reporting and analysis. It enables organizations to create comprehensive dashboards, visualizations, and reports that inform strategic decisions.
Q5: Is Data Warehousing Only Relevant for Large Enterprises?
Answer: No, data warehousing is beneficial for businesses of all sizes. While large enterprises may handle larger datasets, even small and medium-sized businesses can benefit from data warehousing by gaining insights into customer behavior, market trends, and operational efficiency.
Q6: Can Data Warehousing Improve Collaboration Among Departments?
Answer: Absolutely. Data warehousing promotes collaboration by providing a unified platform where different departments can access and analyze the same data. This shared data environment enhances communication and ensures that decisions are based on accurate and consistent information.
Q7: How can I learn more about data mining?
Answer: To delve deeper into data mining, consider studying relevant courses, reading books, participating in online tutorials, and exploring practical applications to gain hands-on experience.
Q8: What is a data miner, and what do they do?
Answer: A data miner is an individual who employs data mining techniques to analyze data, identify patterns, and extract valuable insights for decision-making.
Q9: How does data mining contribute to business success?
Answer: The utilization of data mining empowers businesses to make well-informed decisions, streamline processes for optimization, effectively target marketing efforts, minimize risks, and ultimately gain a competitive edge in the market.
Q10: Is data mining a complex process?
Answer: While data mining involves intricate techniques, its underlying principles can be understood and applied effectively with the proper knowledge and tools.
Conclusion:
Data warehousing in data mining is a cornerstone of modern data-driven decision-making. By consolidating and organizing data, organizations can harness their power to gain insights, make informed choices, and drive business growth.
From basic elements to real-world applications, data warehousing is a pivotal tool that empowers businesses to navigate the complexities of the digital age.
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