Data transformation MCQs January 8, 2026November 18, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is the main purpose of data transformation in the data preprocessing pipeline? (A) To convert the data into a more suitable format for analysis or modeling (B) To reduce the number of features in the dataset (C) To combine data from different sources (D) To remove duplicates and errors in the dataset 2. Which of the following is a common method used for data transformation? (A) Scaling (B) Data imputation (C) Data cleaning (D) Feature extraction 3. What is normalization in the context of data transformation? (A) Rescaling features to a fixed range, usually [0, 1] (B) Converting categorical variables into numeric form (C) Removing duplicate records from the dataset (D) Merging data from multiple sources 4. Which of the following is an example of log transformation? (A) Converting values using the natural logarithm function (B) Adding noise to the data (C) Applying the Min-Max scaling (D) Encoding categorical variables as binary values 5. When is standardization typically used during data transformation? (A) When the data has a skewed distribution (B) When the data needs to be rescaled to a fixed range (C) When the data has a normal distribution and needs to be centered around zero (D) When missing values need to be handled 6. What does log transformation help with in data preprocessing? (A) Scaling features to a common range (B) Increasing the variance of data (C) Handling missing values (D) Reducing the impact of extreme values or skewed distributions 7. What does binning refer to in the context of data transformation? (A) Grouping continuous data into discrete categories or intervals (B) Scaling numerical values to a specific range (C) Encoding categorical variables as numerical values (D) Removing outliers from the data 8. Which of the following is an example of feature extraction during data transformation? (A) Applying one-hot encoding to categorical variables (B) Combining two or more features to create a new feature (C) Selecting the most relevant features using statistical methods (D) Scaling data to a standard range 9. What is the z-score transformation used for in data transformation? (A) Standardizing data to have a mean of 0 and a standard deviation of 1 (B) Rescaling data to a fixed range (C) Removing duplicate entries from the dataset (D) Handling missing values by imputing the mean 10. Which of the following transformation methods is used when the data has a skewed distribution? (A) Standardization (B) One-hot encoding (C) Binning (D) Log transformation 11. In data transformation, what does feature scaling refer to? (A) Reducing the number of features by selecting the most relevant ones (B) Converting categorical data into numerical data (C) Creating new features by combining existing ones (D) Rescaling numerical features to a similar range or distribution 12. What is the purpose of Min-Max scaling in data transformation? (A) To rescale all features into a range between 0 and 1 (B) To reduce the dataset size (C) To eliminate outliers from the data (D) To combine multiple features into a single feature 13. What does one-hot encoding do during data transformation? (A) Scales numerical values to a standard range (B) Converts categorical values into binary columns (C) Combines categorical features into a single column (D) Imputes missing values with the mean 14. Which of the following methods is used to handle skewed data before applying machine learning algorithms? (A) Binning (B) Normalization (C) Log transformation (D) Data imputation 15. What is the result of applying data discretization in data transformation? (A) Creating new features by combining multiple attributes (B) Normalizing the data to a range of [0,1] (C) Converting continuous data into categorical data by grouping values into bins (D) Removing irrelevant features from the dataset