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 only
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