NSCT-Data Collection & Pre-processing MCQs 20 min Score: 0 Attempted: 0/20 Subscribe 1. . Data collection in analytics is: (A) Backup only (B) Encrypting data only (C) Compressing files only (D) The process of gathering relevant and accurate data from various sourcesShow All Answers 2. . Primary data sources include: (A) Data warehouses only (B) Databases only (C) Surveys, interviews, experiments, and observations (D) Backup only 3. . Secondary data sources include: (A) Experiments only (B) Personal interviews only (C) Existing datasets, reports, publications, and online databases (D) Backup only 4. . Data pre-processing is important because: (A) Encrypting data (B) Raw data often contains noise, missing values, and inconsistencies (C) Compressing data (D) Backup only 5. . Data cleaning involves: (A) Backup only (B) Encrypting data (C) Compressing data (D) Removing duplicates, correcting errors, and handling missing values 6. . Data normalization is: (A) Compressing values (B) Encrypting numbers (C) Scaling data to a specific range to improve model performance (D) Backup only 7. . Data transformation includes: (A) Backup only (B) Encrypting transformations (C) Compressing transformations (D) Converting data formats, encoding categorical variables, and aggregating values 8. . Handling missing values can be done by: (A) Backup only (B) Encrypting missing values (C) Compressing missing values (D) Removing rows, filling with mean/median/mode, or using predictive imputation 9. . Outlier detection in pre-processing helps to: (A) Backup only (B) Encrypt outliers (C) Compress outliers (D) Identify and handle data points that deviate significantly from the rest 10. . Feature selection is: (A) Encrypting features (B) Choosing relevant variables to reduce dimensionality and improve model accuracy (C) Compressing features (D) Backup only 11. . Feature extraction is: (A) Backup only (B) Encrypting features (C) Compressing features (D) Creating new features from existing data to better represent patterns 12. . Data integration involves: (A) Encrypting integration (B) Combining data from multiple sources into a unified dataset (C) Compressing integration (D) Backup only 13. . Data reduction techniques include: (A) Dimensionality reduction, sampling, and aggregation (B) Encrypting data (C) Compressing data only (D) Backup only 14. . One-hot encoding is used to: (A) Compress categories (B) Encrypt categories (C) Convert categorical variables into binary vectors (D) Backup only 15. . Z-score standardization is: (A) Scaling data based on mean and standard deviation (B) Encrypting z-scores (C) Compressing z-scores (D) Backup only 16. . Data discretization is: (A) Converting continuous data into intervals or categories (B) Encrypting intervals (C) Compressing categories (D) Backup only 17. . Noise in data refers to: (A) Compressing noise (B) Encrypting errors (C) Random errors or irrelevant information in datasets (D) Backup only 18. . Data pre-processing improves: (A) Encrypting models (B) Accuracy, efficiency, and performance of AI and ML models (C) Compressing models (D) Backup only 19. . Sampling in data pre-processing is used to: (A) Backup only (B) Encrypt samples (C) Compress samples (D) Reduce dataset size while maintaining representative information 20. . The main purpose of data collection and pre-processing is to: (A) Compress files only (B) Encrypt data only (C) Obtain clean, accurate, and structured data ready for analysis and model building (D) Backup only
