NSCT – Python for AI & Data Analytics MCQs 20 min Score: 0 Attempted: 0/20 Subscribe 1. . Python is popular in AI and data analytics because: (A) Compressing files only (B) Encrypting data only (C) It has simple syntax, extensive libraries, and strong community support (D) Backup onlyShow All Answers 2. . NumPy in Python is used for: (A) Backup only (B) Encrypting numbers (C) Compressing arrays (D) Efficient numerical computations and handling multidimensional arrays 3. . Pandas library in Python is used for: (A) Backup only (B) Encrypting dataframes (C) Compressing dataframes (D) Data manipulation, analysis, and working with dataframes 4. . Matplotlib in Python is used for: (A) Compressing plots (B) Encrypting plots (C) Data visualization and plotting graphs (D) Backup only 5. . Seaborn library in Python is: (A) A statistical data visualization library based on Matplotlib (B) Encrypting statistical charts (C) Compressing visualizations (D) Backup only 6. . Scikit-learn is used for: (A) Compressing ML models (B) Encrypting ML models (C) Machine learning algorithms like classification, regression, and clustering (D) Backup only 7. . TensorFlow and PyTorch are used for: (A) Encrypting neural networks (B) Deep learning and neural network implementations (C) Compressing deep learning models (D) Backup only 8. . In Python, a DataFrame is: (A) Encrypting data frame (B) A 2-dimensional labeled data structure for rows and columns (C) Compressing table (D) Backup only 9. . Reading CSV files in Python can be done using: (A) Matplotlib read_csv() (B) NumPy load_csv() (C) Pandas function read_csv() (D) Backup only 10. . Handling missing values in data can be done using: (A) dropna() or fillna() in Pandas (B) encryptna() only (C) compressna() only (D) Backup only 11. . Data preprocessing in Python for AI includes: (A) Encrypting data (B) Cleaning, normalizing, encoding categorical variables, and feature scaling (C) Compressing data (D) Backup only 12. . Feature selection in Python helps to: (A) Compress features (B) Encrypt features (C) Reduce dimensionality and improve model performance (D) Backup only 13. . Splitting data into training and testing sets in Python can be done using: (A) train_test_split() from scikit-learn (B) split_data() only (C) divide_data() only (D) Backup only 14. . Python lists and dictionaries are used for: (A) Compressing data (B) Encrypting data (C) Storing and organizing data for processing (D) Backup only 15. . Python loops and conditional statements are used to: (A) Compress flow (B) Encrypt flow (C) Control the flow of data processing and algorithms (D) Backup only 16. . Python functions are useful in AI and data analytics because: (A) They allow code reuse, modularity, and better readability (B) Encrypt functions (C) Compress functions (D) Backup only 17. . Lambda functions in Python are: (A) Backup only (B) Encrypting lambda operations (C) Compressing functions (D) Anonymous functions used for small and simple operations 18. . In Python, the .groupby() function is used to: (A) Backup only (B) Encrypt groups (C) Compress groups (D) Aggregate and summarize data based on categories 19. . Python's apply() function helps to: (A) Encrypt operations (B) Apply a function to each element or row/column of a DataFrame (C) Compress operations (D) Backup only 20. . The main purpose of Python in AI and Data Analytics is to: (A) Encrypt data only (B) Perform data analysis, preprocessing, visualization, and implement machine learning and AI models efficiently (C) Compress files only (D) Backup only
