Data Transformation MCQs – T4Tutorials.com

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1. : What is data transformation?
(A) Deleting irrelevant data from the dataset
(B) Converting data into a suitable format for analysis
(C) Aggregating data from multiple sources into a unified format
(D) Generating new data from existing datasets

2. : Which of the following is a common data transformation task?
(A) Adding noise to the dataset
(B) Deleting missing values
(C) Removing duplicate records
(D) Ignoring outlier detection

3. : Why is data transformation necessary in data mining?
(A) To increase the size of the dataset
(B) To reduce the dimensionality of the dataset
(C) To convert data into a format suitable for analysis
(D) To introduce noise into the dataset

4. : Which technique involves scaling numerical data to a standard range?
(A) Data normalization
(B) Data discretization
(C) Data encoding
(D) Data imputation

5. : What does data discretization involve?
(A) Converting continuous data values into categorical values
(B) Converting categorical data values into continuous values
(C) Adding noise to the dataset
(D) Removing missing values from the dataset

6. : Which of the following is a method of data discretization?
(A) Mean normalization
(B) Min-max scaling
(C) Binning
(D) Z-score normalization

7. : How does data transformation help in handling skewed data distributions?
(A) By removing outliers
(B) By normalizing the data
(C) By adding noise to the dataset
(D) By ignoring missing values

8. : What is the purpose of feature scaling in data transformation?
(A) To remove duplicate records
(B) To convert data into a standard format
(C) To handle missing values
(D) To introduce variability into the dataset

9. : Which technique involves transforming categorical variables into numerical representations?
(A) Data normalization
(B) Data encoding
(C) Data discretization
(D) Data imputation

10. : What is the primary goal of data transformation in data mining?
(A) To introduce noise into the dataset
(B) To increase the dimensionality of the dataset
(C) To enhance the quality of data visualization
(D) To prepare data for analysis

11. : Which technique involves applying mathematical transformations to data to make it more suitable for analysis?
(A) Data aggregation
(B) Data standardization
(C) Data normalization
(D) Data enrichment

12. : What does logarithmic transformation aim to achieve?
(A) To convert data into a linear format
(B) To handle missing values in the dataset
(C) To reduce the skewness of data distributions
(D) To introduce variability into the dataset

13. : Which of the following is a technique for handling outliers during data transformation?
(A) Data imputation
(B) Data masking
(C) Data validation
(D) Data trimming

14. : How does feature engineering relate to data transformation?
(A) Feature engineering is a subset of data transformation techniques
(B) Feature engineering involves creating new features from existing data
(C) Feature engineering focuses solely on data cleaning
(D) Feature engineering removes outliers from the dataset

15. : Which approach involves transforming data into a format that improves the performance of machine learning algorithms?
(A) Data standardization
(B) Data normalization
(C) Feature scaling
(D) All of the above

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

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