Data Cleaning MCQs – T4Tutorials.com

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1. : What is data cleaning?
(A) Deleting all data points that do not fit the model
(B) Preparing data for analysis by removing errors and inconsistencies
(C) Extracting useful information from the dataset
(D) Aggregating data from various sources

2. : Which of the following is not a step in data cleaning?
(A) Removing duplicates
(B) Handling missing values
(C) Transforming data into a different format
(D) Adding noise to the dataset

3. : What is the primary goal of removing duplicate records in data cleaning?
(A) To reduce computational complexity
(B) To increase the size of the dataset
(C) To improve the accuracy of analysis
(D) To add variability to the dataset

4. : Which technique is used to detect and handle missing values in a dataset?
(A) Data normalization
(B) Data discretization
(C) Data imputation
(D) Data transformation

5. : Why is handling missing data crucial in data mining?
(A) Missing data does not affect the analysis results
(B) It ensures statistical models perform optimally
(C) It reduces the need for data cleaning
(D) Missing data can be safely ignored

6. : Which approach involves replacing missing values with the mean or median of the non-missing values in the same column?
(A) Mean normalization
(B) Median normalization
(C) Central tendency imputation
(D) Standardization

7. : What does outlier detection aim to identify in a dataset?
(A) Anomalies or data points that significantly deviate from other observations
(B) Missing values in the dataset
(C) Unstructured data points
(D) Data points that fit the model perfectly

8. : Which method is used to identify and correct inconsistencies in data values that do not fit predefined rules?
(A) Data validation
(B) Data imputation
(C) Data integration
(D) Data transformation

9. : What is the purpose of data normalization in data cleaning?
(A) To remove outliers from the dataset
(B) To transform data into a consistent format for analysis
(C) To introduce variability into the dataset
(D) To handle missing values

10. : Which technique involves converting categorical data into numerical values for analysis?
(A) Data discretization
(B) Data standardization
(C) Data encoding
(D) Data imputation

11. : What is the primary purpose of data integration in data cleaning?
(A) To aggregate data from multiple sources into a unified format
(B) To remove outliers from the dataset
(C) To transform data into a consistent format
(D) To add noise to the dataset

12. : Which of the following is a common approach to handling noisy data in data cleaning?
(A) Ignoring noisy data during analysis
(B) Adding more noise to balance it out
(C) Applying outlier detection techniques
(D) None of the above

13. : What does data standardization aim to achieve?
(A) To convert data into a standard format for consistency
(B) To add variability to the dataset
(C) To delete duplicate records
(D) To replace missing values

14. : Which technique is used to transform data into a common scale without distorting differences in the ranges of values?
(A) Min-max scaling
(B) Z-score normalization
(C) Mean normalization
(D) Central tendency imputation

15. : Why is data cleaning considered a critical step in the data mining process?
(A) It improves the efficiency of data storage
(B) It reduces the amount of data needed for analysis
(C) It enhances the quality and reliability of analysis results
(D) It automates the process of data collection

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By: Prof. Dr. Fazal Rehman | Last updated: May 14, 2025

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