Data reduction techniques MCQs January 8, 2026November 18, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is the primary goal of data reduction in data preprocessing? (A) To remove missing values from the dataset (B) To increase the dataset’s complexity (C) To reduce the size of the dataset without losing significant information (D) To convert data into a more suitable format for analysis 2. Which of the following is a common data reduction technique? (A) Feature selection (B) Data imputation (C) One-hot encoding (D) Normalization 3. Principal Component Analysis (PCA) is an example of which type of data reduction technique? (A) Feature extraction (B) Feature selection (C) Data transformation (D) Data integration 4. What does feature selection aim to achieve in the context of data reduction? (A) Scaling features to a common range (B) Removing duplicates from the dataset (C) Reducing the size of the dataset by removing entire rows (D) Reducing the number of features by selecting only the most relevant ones 5. Which of the following is an example of instance reduction in data reduction? (A) Applying dimensionality reduction methods like PCA (B) Removing irrelevant features from the dataset (C) Scaling features to a standard range (D) Reducing the number of instances by selecting a subset of data 6. Which technique is used to remove unimportant or irrelevant features from the dataset? (A) Normalization (B) Feature selection (C) One-hot encoding (D) Data cleaning 7. What does dimensionality reduction refer to in the context of data reduction? (A) Reducing the number of input variables or features (B) Removing duplicate rows from the dataset (C) Scaling data to a uniform range (D) Combining features into a single feature 8. Principal Component Analysis (PCA) works by: (A) Removing missing values (B) Selecting features based on importance (C) Projecting data into a lower-dimensional space while retaining variance (D) Normalizing data into a fixed range 9. Which of the following is a potential disadvantage of data reduction techniques? (A) The dataset becomes larger and more complex (B) The original relationships between the data points may be lost (C) The data becomes more redundant (D) It increases the computation time for model training 10. In which scenario would you typically use data reduction techniques? (A) When dealing with high-dimensional data (B) When the dataset has too many instances (C) When the data contains outliers (D) When you want to visualize the data in 3D 11. What is the main purpose of data compression as a data reduction technique? (A) To represent the data in a more compact form without losing information (B) To reduce the dimensionality of data (C) To eliminate missing values (D) To encode categorical variables as numerical values 12. Discretization in data reduction refers to: (A) Reducing the number of data instances (B) Removing outliers from the dataset (C) Scaling all features to a uniform range (D) Converting continuous features into categorical ones by grouping values into bins 13. Which of the following is a method used for reducing the number of features? (A) K-means clustering (B) Outlier detection (C) Linear regression (D) Principal Component Analysis (PCA) 14. What is feature extraction in the context of data reduction? (A) Selecting a subset of important features from the original set (B) Removing redundant features (C) Normalizing the data (D) Creating new features by combining or transforming existing ones 15. K-means clustering can be used as a data reduction technique in the following way: (A) By grouping similar instances together and reducing the number of data points (B) By selecting the most important clusters to retain (C) By reducing the number of features to a lower dimension (D) By transforming data into a uniform scale