20 Data Preprocessing Quiz Questions and Answers

1. Question: What is the primary purpose of handling missing data in data preprocessing?
A) To increase the dataset size
B) To ensure complete and accurate analysis
C) To add random noise to the data
D) To convert data types
Answer: B
Explanation: Handling missing data prevents bias and errors in models by filling gaps or removing incomplete entries, ensuring the dataset is reliable for analysis.

2. Question: Which technique is commonly used to deal with missing values in a dataset?
A) Data normalization
B) Imputation
C) Feature selection
D) Outlier detection
Answer: B
Explanation: Imputation replaces missing values with estimated ones, such as mean or median, maintaining the dataset’s integrity without losing information.

3. Question: In data preprocessing, what does normalization typically achieve?
A) Reduces the number of features
B) Scales data to a specific range, like 0 to 1
C) Encodes categorical variables
D) Removes duplicate rows
Answer: B
Explanation: Normalization rescales features to a common scale, preventing features with larger ranges from dominating the model during training.

4. Question: Which method is used for encoding categorical variables into numerical form?
A) Standardization
B) One-hot encoding
C) Data aggregation
D) Discretization
Answer: B
Explanation: One-hot encoding converts categorical variables into a binary format, allowing machine learning algorithms to process them effectively.

5. Question: What is the main goal of feature scaling in data preprocessing?
A) To remove irrelevant features
B) To standardize the range of independent variables
C) To handle multicollinearity
D) To split the dataset
Answer: B
Explanation: Feature scaling ensures all features contribute equally to the model by adjusting them to a similar scale, improving algorithm performance.

6. Question: How does outlier detection contribute to data preprocessing?
A) By increasing data variability
B) By identifying and handling anomalous data points
C) By encoding data types
D) By merging datasets
Answer: B
Explanation: Outlier detection helps remove or adjust extreme values that could skew the results of statistical models and analyses.

7. Question: Which technique involves combining multiple datasets into one?
A) Data cleaning
B) Data integration
C) Data normalization
D) Data reduction
Answer: B
Explanation: Data integration merges data from different sources, ensuring consistency and completeness for comprehensive analysis.

8. Question: What is discretization in data preprocessing?
A) Converting continuous data into discrete bins
B) Scaling data to zero mean
C) Removing missing values
D) Encoding binary data
Answer: A
Explanation: Discretization transforms continuous variables into categorical ones by dividing them into intervals, simplifying analysis for certain algorithms.

9. Question: Why is data cleaning important before modeling?
A) To add more features
B) To correct errors, inconsistencies, and redundancies
C) To increase computational speed
D) To visualize data
Answer: B
Explanation: Data cleaning removes inaccuracies like duplicates or errors, ensuring the dataset is accurate and ready for reliable modeling.

10. Question: Which method is used for handling class imbalance in datasets?
A) Oversampling
B) Data normalization
C) Feature extraction
D) Outlier removal
Answer: A
Explanation: Oversampling increases the representation of minority classes, preventing biased models that favor the majority class.

11. Question: In preprocessing, what does standardization do?
A) Converts categorical to numerical data
B) Rescales data to have a mean of 0 and standard deviation of 1
C) Merges datasets
D) Detects missing values
Answer: B
Explanation: Standardization transforms data to a standard normal distribution, making it suitable for algorithms sensitive to feature scales.

12. Question: What is the purpose of feature selection?
A) To add more variables
B) To select the most relevant features and reduce dimensionality
C) To encode data
D) To handle missing data
Answer: B
Explanation: Feature selection improves model efficiency by eliminating irrelevant or redundant features, reducing overfitting and computation time.

13. Question: Which approach is used for data transformation, such as log transformation?
A) To make data symmetric and normalize distribution
B) To remove features
C) To detect outliers
D) To integrate datasets
Answer: A
Explanation: Log transformation stabilizes variance and makes skewed data more normally distributed, aiding in better model performance.

14. Question: How does binning help in data preprocessing?
A) By grouping continuous data into bins for simplification
B) By scaling numerical values
C) By encoding categories
D) By merging data sources
Answer: A
Explanation: Binning converts continuous data into discrete intervals, reducing noise and making patterns easier to identify.

15. Question: What is the role of data aggregation in preprocessing?
A) To summarize data at a higher level, like averages
B) To remove duplicates
C) To normalize values
D) To split data into training and testing sets
Answer: A
Explanation: Data aggregation combines data points into summaries, reducing volume while retaining essential information for analysis.

16. Question: Which technique addresses multicollinearity in features?
A) Data imputation
B) Principal Component Analysis (PCA)
C) One-hot encoding
D) Discretization
Answer: B
Explanation: PCA reduces multicollinearity by transforming correlated features into uncorrelated components, simplifying the dataset.

17. Question: Why is it important to handle noisy data during preprocessing?
A) To increase dataset size
B) To eliminate errors that could affect model accuracy
C) To encode variables
D) To visualize patterns
Answer: B
Explanation: Handling noisy data removes or smooths out inaccuracies, ensuring the model learns from reliable patterns rather than errors.

18. Question: What does data reduction aim to achieve?
A) To expand the dataset
B) To decrease data size while preserving key information
C) To standardize features
D) To detect outliers
Answer: B
Explanation: Data reduction techniques like dimensionality reduction minimize storage and computation needs without losing critical data insights.

19. Question: In preprocessing, how is label encoding different from one-hot encoding?
A) It assigns a unique integer to each category
B) It creates binary columns for each category
C) It scales the data
D) It removes missing values
Answer: A
Explanation: Label encoding simplifies categorical data by converting it to integers, which is useful for ordinal data but may imply order where none exists.

20. Question: Which step in data preprocessing involves splitting data into training and testing sets?
A) Data cleaning
B) Data partitioning
C) Feature scaling
D) Normalization
Answer: B
Explanation: Data partitioning divides the dataset to evaluate model performance on unseen data, preventing overfitting and ensuring generalizability.