1. What is data mining?
A) The process of cleaning data
B) The process of discovering patterns in large datasets
C) The process of storing data
D) The process of visualizing data
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2. What is one key difference between data mining and big data analytics?
A) Data mining focuses only on structured data
B) Big data analytics is only for real-time data
C) Data mining is a subset of big data analytics
D) Big data analytics does not require data cleaning
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2. Which of the following best describes Big Data?
A) Data that is too large for traditional databases
B) Data that is stored in a cloud
C) Data that can be easily analyzed
D) Data collected from a single source
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4. Which of the following is an application of data mining?
A) Forecasting sales
B) Conducting surveys
C) Data entry
D) Data storage
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5. What is a major issue in data mining?
A) Lack of data storage
B) Data quality and integrity
C) Limited software availability
D) Ineffective visualization tools
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6. Which of the following is NOT a challenge in data mining?
A) Scalability of algorithms
B) Privacy concerns
C) Data redundancy
D) The simplicity of algorithms
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7. Machine learning algorithms are often used in data mining to:
A) Store data
B) Clean data
C) Make predictions based on patterns
D) Visualize data
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8. Which of the following is a technique used in data mining?
A) Data normalization
B) Data redundancy
C) Data archiving
D) Data cleansing
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9. Which of the following is an application of machine learning in data mining?
A) Customer segmentation
B) Manual data entry
C) Data backup
D) Data storage optimization
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10. What does the term “overfitting” refer to in data mining?
A) Model performing well on unseen data
B) Model performing poorly on training data
C) Model being too complex and capturing noise
D) Model being too simple
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11. What is data integration in the context of data mining?
A) Combining data from different sources into a single dataset
B) Cleaning data from a single source
C) Storing data in a database
D) Visualizing data in reports
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12. Which of the following is a common pitfall in data mining?
A) Using too much data
B) Ignoring data privacy
C) Using simple algorithms
D) All of the above
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11. What is data integration in the context of data mining?
A) Combining data from different sources into a single dataset
B) Cleaning data from a single source
C) Storing data in a database
D) Visualizing data in reports
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13. Which of the following represents a key opportunity in data mining?
A) Reducing data complexity
B) Improving decision-making processes
C) Simplifying data collection
D) Decreasing storage needs
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14. What is a typical step in the data mining process?
A) Data encryption
B) Data collection
C) Data destruction
D) Data visualization
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15. Which type of data is primarily analyzed in data mining?
A) Structured data only
B) Unstructured data only
C) Both structured and unstructured data
D) Only quantitative data
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16. What is the role of descriptive statistics in data mining?
A) Predicting future trends
B) Summarizing and describing the main features of data
C) Cleaning data
D) Integrating data
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17. Which of the following best describes “data discretization”?
A) Converting continuous data into categorical data
B) Combining multiple datasets
C) Removing duplicates from data
D) Storing data securely
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18. What is a major benefit of using data mining techniques?
A) Increased data storage costs
B) Improved data accuracy
C) Enhanced data complexity
D) Reduced data access speed
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19. Which of the following describes “data reduction”?
A) Increasing the size of the dataset
B) Reducing the amount of data while maintaining its integrity
C) Simplifying complex algorithms
D) Merging different datasets
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20. In data mining, the term "pattern recognition" refers to:
A) The process of identifying valid patterns in data
B) The elimination of outliers from the dataset
C) Data visualization techniques
D) Data encryption methods
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