{"id":912,"date":"2025-01-19T03:20:11","date_gmt":"2025-01-19T03:20:11","guid":{"rendered":"https:\/\/itxperts.co.in\/blog\/?p=912"},"modified":"2025-08-02T09:05:33","modified_gmt":"2025-08-02T09:05:33","slug":"100-important-questions-and-answers-for-cbse-class-x-artificial-intelligence-ai-code-417-board-exam","status":"publish","type":"post","link":"https:\/\/itxperts.co.in\/blog\/100-important-questions-and-answers-for-cbse-class-x-artificial-intelligence-ai-code-417-board-exam\/","title":{"rendered":"100 Important Questions and Answers for CBSE Class X Artificial Intelligence (AI-417) Board Exam"},"content":{"rendered":"\n

Artificial Intelligence (AI) is a crucial subject for CBSE Class X students, focusing on real-world applications, ethics, and programming skills. To help you excel in your AI (417) board exams, here are 100 chapter-wise important questions and answers<\/strong> organized in blog post format. These cover key topics from the curriculum, including theory, practical, and project-related queries.<\/p>\n\n\n\n

\n\n\n\n

Chapter 1: Introduction to Artificial Intelligence (AI)<\/strong><\/h3>\n\n\n\n

1. What is Artificial Intelligence (AI)?<\/strong>
AI refers to the simulation of human intelligence in machines that are programmed to think, learn, and solve problems like humans.<\/p>\n\n\n\n

2. Name the three main domains of AI.<\/strong><\/p>\n\n\n\n

    \n
  • Data Science<\/li>\n\n\n\n
  • Computer Vision<\/li>\n\n\n\n
  • Natural Language Processing (NLP)<\/li>\n<\/ul>\n\n\n\n

    3. List some real-life applications of AI.<\/strong><\/p>\n\n\n\n

      \n
    • Virtual assistants like Siri and Alexa<\/li>\n\n\n\n
    • Self-driving cars<\/li>\n\n\n\n
    • Personalized recommendations on platforms like Netflix<\/li>\n<\/ul>\n\n\n\n

      4. What are AI ethics?<\/strong>
      AI ethics deal with the moral principles guiding the development and use of AI, such as fairness, transparency, and privacy.<\/p>\n\n\n\n

      5. How does AI help achieve Sustainable Development Goals (SDGs)?<\/strong>
      AI aids in addressing global challenges, such as improving healthcare, optimizing agriculture, and combating climate change.<\/p>\n\n\n\n

      \n\n\n\n

      Chapter 2: AI Project Cycle<\/strong><\/h3>\n\n\n\n

      6. What are the stages of the AI Project Cycle?<\/strong><\/p>\n\n\n\n

        \n
      • Problem Scoping<\/li>\n\n\n\n
      • Data Acquisition<\/li>\n\n\n\n
      • Data Exploration<\/li>\n\n\n\n
      • Modeling<\/li>\n\n\n\n
      • Evaluation<\/li>\n<\/ul>\n\n\n\n

        7. What is the importance of problem scoping in an AI project?<\/strong>
        Problem scoping defines the project goals, objectives, and constraints, ensuring a clear direction for the AI model.<\/p>\n\n\n\n

        8. Explain the term “Data Acquisition.”<\/strong>
        Data acquisition is the process of collecting relevant and reliable data for training and testing an AI model.<\/p>\n\n\n\n

        9. What is data visualization? Why is it important?<\/strong>
        Data visualization involves graphical representation of data. It helps in identifying patterns, trends, and insights for decision-making.<\/p>\n\n\n\n

        10. What does “Evaluation” mean in the AI Project Cycle?<\/strong>
        Evaluation assesses the performance of an AI model using metrics like accuracy, precision, recall, and F1 score.<\/p>\n\n\n\n

        \n\n\n\n

        Chapter 3: Advanced Python<\/strong><\/h3>\n\n\n\n

        11. Define a variable in Python.<\/strong>
        A variable is a container for storing data values in a program.<\/p>\n\n\n\n

        12. What is the difference between a list and a tuple in Python?<\/strong><\/p>\n\n\n\n

          \n
        • List<\/strong>: Mutable (can be changed), e.g., [1, 2, 3]<\/code><\/li>\n\n\n\n
        • Tuple<\/strong>: Immutable (cannot be changed), e.g., (1, 2, 3)<\/code><\/li>\n<\/ul>\n\n\n\n

          13. Write a Python program to calculate the sum of two numbers.<\/strong><\/p>\n\n\n\n

          a = 5\nb = 10\nprint(\"Sum:\", a + b)\n<\/code><\/pre>\n\n\n\n
          14. Name three Python libraries commonly used in AI.<\/strong><\/p>\n\n\n\n
            \n
          • NumPy<\/li>\n\n\n\n
          • Pandas<\/li>\n\n\n\n
          • Matplotlib<\/li>\n<\/ul>\n\n\n\n
            15. What is the purpose of Jupyter Notebook?<\/strong>
            Jupyter Notebook is an open-source tool for writing, testing, and sharing Python code in an interactive format.<\/p>\n\n\n\n
            \n\n\n\n
            Chapter 4: Data Science<\/strong><\/h3>\n\n\n\n
            16. Define data science.<\/strong>
            Data science is the study of data to extract meaningful insights using techniques like analysis, visualization, and modeling.<\/p>\n\n\n\n
            17. What is NumPy used for in Python?<\/strong>
            NumPy is used for numerical computations, such as array operations and mathematical functions.<\/p>\n\n\n\n
            18. Write a Python program to calculate the mean of a dataset.<\/strong><\/p>\n\n\n\n
            import numpy as np\ndata = [10, 20, 30, 40]\nmean = np.mean(data)\nprint(\"Mean:\", mean)\n<\/code><\/pre>\n\n\n\n
            19. Explain the term “data exploration.”<\/strong>
            Data exploration involves examining data sets to summarize their characteristics, often using statistical tools.<\/p>\n\n\n\n
            20. What are the common types of graphs used in data visualization?<\/strong><\/p>\n\n\n\n
              \n
            • Line chart<\/li>\n\n\n\n
            • Bar graph<\/li>\n\n\n\n
            • Scatter plot<\/li>\n<\/ul>\n\n\n\n
              \n\n\n\n
              Chapter 5: Computer Vision<\/strong><\/h3>\n\n\n\n
              21. What is Computer Vision (CV)?<\/strong>
              CV is a field of AI that enables machines to interpret and analyze visual information from images or videos.<\/p>\n\n\n\n
              22. Explain the term “pixel.”<\/strong>
              A pixel is the smallest unit of a digital image, representing a single point of color.<\/p>\n\n\n\n
              23. What is the purpose of OpenCV in AI?<\/strong>
              OpenCV is an open-source library used for image processing and computer vision tasks.<\/p>\n\n\n\n
              24. Write a Python program to read and display an image using OpenCV.<\/strong><\/p>\n\n\n\n
              import cv2\nimage = cv2.imread('image.jpg')\ncv2.imshow('Image', image)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
              25. What are RGB images?<\/strong>
              RGB images use three color channels\u2014Red, Green, and Blue\u2014to represent colors in a digital image.<\/p>\n\n\n\n
              \n\n\n\n
              Chapter 6: Natural Language Processing (NLP)<\/strong><\/h3>\n\n\n\n
              26. Define Natural Language Processing.<\/strong>
              NLP is a field of AI that enables machines to understand, interpret, and generate human language.<\/p>\n\n\n\n
              27. What is tokenization in NLP?<\/strong>
              Tokenization is the process of breaking a text into smaller units like words or sentences.<\/p>\n\n\n\n
              28. Write a Python program to tokenize a sentence using NLTK.<\/strong><\/p>\n\n\n\n
              from nltk.tokenize import word_tokenize\nsentence = \"AI is transforming the world.\"\ntokens = word_tokenize(sentence)\nprint(tokens)\n<\/code><\/pre>\n\n\n\n
              29. Explain the Bag-of-Words model in NLP.<\/strong>
              The Bag-of-Words model represents text data as a collection of words and their frequency, ignoring grammar and word order.<\/p>\n\n\n\n
              30. List some applications of NLP in daily life.<\/strong><\/p>\n\n\n\n
                \n
              • Chatbots<\/li>\n\n\n\n
              • Sentiment analysis<\/li>\n\n\n\n
              • Translation tools<\/li>\n<\/ul>\n\n\n\n
                \n\n\n\n
                Chapter 7: Evaluation<\/strong><\/h3>\n\n\n\n
                31. What is model evaluation?<\/strong>
                Model evaluation measures the performance of an AI model using specific metrics.<\/p>\n\n\n\n
                32. Define accuracy, precision, recall, and F1 score.<\/strong><\/p>\n\n\n\n
                  \n
                • Accuracy:<\/strong> Percentage of correct predictions.<\/li>\n\n\n\n
                • Precision:<\/strong> Ratio of true positives to all predicted positives.<\/li>\n\n\n\n
                • Recall:<\/strong> Ratio of true positives to all actual positives.<\/li>\n\n\n\n
                • F1 Score:<\/strong> Harmonic mean of precision and recall.<\/li>\n<\/ul>\n\n\n\n
                  33. Write an example of a confusion matrix.<\/strong><\/p>\n\n\n\n
                  Predicted<\/strong><\/th>Positive<\/strong><\/th>Negative<\/strong><\/th><\/tr><\/thead>
                  Positive<\/td>True Positive<\/td>False Negative<\/td><\/tr>
                  Negative<\/td>False Positive<\/td>True Negative<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                  34. Why is a confusion matrix important?<\/strong>
                  It provides a detailed breakdown of model predictions, helping to identify errors and areas for improvement.<\/p>\n\n\n\n
                  35. What is underfitting and overfitting?<\/strong><\/p>\n\n\n\n
                    \n
                  • Underfitting:<\/strong> Model is too simple and performs poorly.<\/li>\n\n\n\n
                  • Overfitting:<\/strong> Model is too complex and performs well on training data but poorly on new data.<\/li>\n<\/ul>\n\n\n\n
                    \n\n\n\n
                    More Practice Questions by Topic<\/strong><\/h3>\n\n\n\n
                    Problem-Solving in AI<\/strong><\/h4>\n\n\n\n
                      \n
                    1. What is supervised learning?<\/li>\n\n\n\n
                    2. Explain reinforcement learning with an example.<\/li>\n<\/ol>\n\n\n\n
                      Programming with Python<\/strong><\/h4>\n\n\n\n
                        \n
                      1. Write a program to calculate the median using NumPy.<\/li>\n\n\n\n
                      2. How do you create a scatter plot in Matplotlib?<\/li>\n<\/ol>\n\n\n\n
                        Real-Life Applications<\/strong><\/h4>\n\n\n\n
                          \n
                        1. How does AI contribute to healthcare?<\/li>\n\n\n\n
                        2. What role does AI play in climate change solutions?<\/li>\n<\/ol>\n\n\n\n
                          \n\n\n\n
                          Chapter 7: Evaluation (Continued)<\/strong><\/h3>\n\n\n\n
                          36. Why is the F1 score important in evaluating AI models?<\/strong>
                          The F1 score balances precision and recall, making it useful when dealing with imbalanced datasets.<\/p>\n\n\n\n
                          37. What is the difference between validation and testing in model evaluation?<\/strong><\/p>\n\n\n\n
                            \n
                          • Validation:<\/strong> Used during model training to tune parameters.<\/li>\n\n\n\n
                          • Testing:<\/strong> Used after training to measure the model\u2019s final performance.<\/li>\n<\/ul>\n\n\n\n
                            38. Write a formula to calculate precision.<\/strong>
                            Precision=True PositivesTrue Positives + False Positives\\text{Precision} = \\frac{\\text{True Positives}}{\\text{True Positives + False Positives}}<\/p>\n\n\n\n
                            39. What is the role of a confusion matrix?<\/strong>
                            A confusion matrix evaluates a model by showing the number of true\/false positives and true\/false negatives.<\/p>\n\n\n\n
                            40. How do you determine if a model is overfitting?<\/strong>
                            If a model performs well on training data but poorly on validation or test data, it is overfitting.<\/p>\n\n\n\n
                            \n\n\n\n
                            Advanced Python Programming (Additional Questions)<\/strong><\/h3>\n\n\n\n
                            41. How do you create a virtual environment in Python?<\/strong>
                            Run the following command:<\/p>\n\n\n\n
                            python -m venv env_name\n<\/code><\/pre>\n\n\n\n
                            42. What is the purpose of the matplotlib<\/code> library?<\/strong>
                            Matplotlib is used for creating static, interactive, and animated visualizations in Python.<\/p>\n\n\n\n
                            43. Write a Python program to create a line graph using Matplotlib.<\/strong><\/p>\n\n\n\n
                            import matplotlib.pyplot as plt\nx = [1, 2, 3, 4]\ny = [10, 20, 25, 30]\nplt.plot(x, y)\nplt.title(\"Line Graph Example\")\nplt.show()\n<\/code><\/pre>\n\n\n\n
                            44. How do you import the Pandas library in Python?<\/strong>
                            Use the command:<\/p>\n\n\n\n
                            import pandas as pd\n<\/code><\/pre>\n\n\n\n
                            45. What is the difference between df.head()<\/code> and df.tail()<\/code> in Pandas?<\/strong><\/p>\n\n\n\n
                              \n
                            • df.head()<\/code><\/strong>: Displays the first 5 rows of a DataFrame.<\/li>\n\n\n\n
                            • df.tail()<\/code><\/strong>: Displays the last 5 rows of a DataFrame.<\/li>\n<\/ul>\n\n\n\n
                              \n\n\n\n
                              Data Science (Additional Questions)<\/strong><\/h3>\n\n\n\n
                              46. What is the difference between structured and unstructured data?<\/strong><\/p>\n\n\n\n
                                \n
                              • Structured Data:<\/strong> Organized in a fixed format, like rows and columns (e.g., databases).<\/li>\n\n\n\n
                              • Unstructured Data:<\/strong> Does not follow a specific format (e.g., images, videos).<\/li>\n<\/ul>\n\n\n\n
                                47. Write a Python program to calculate the standard deviation of a dataset.<\/strong><\/p>\n\n\n\n
                                import numpy as np\ndata = [10, 20, 30, 40]\nstd_dev = np.std(data)\nprint(\"Standard Deviation:\", std_dev)\n<\/code><\/pre>\n\n\n\n
                                48. What is a CSV file, and why is it important in data science?<\/strong>
                                A CSV (Comma-Separated Values) file is a simple file format used to store tabular data, making it easy to import and manipulate in Python.<\/p>\n\n\n\n
                                49. What are the key steps in data preprocessing?<\/strong><\/p>\n\n\n\n
                                  \n
                                • Cleaning<\/li>\n\n\n\n
                                • Normalization<\/li>\n\n\n\n
                                • Transformation<\/li>\n\n\n\n
                                • Feature selection<\/li>\n<\/ul>\n\n\n\n
                                  50. Define outlier detection in data analysis.<\/strong>
                                  Outlier detection involves identifying data points that differ significantly from the majority of the dataset.<\/p>\n\n\n\n
                                  \n\n\n\n
                                  Computer Vision (Additional Questions)<\/strong><\/h3>\n\n\n\n
                                  51. What are the basic tasks in computer vision?<\/strong><\/p>\n\n\n\n
                                    \n
                                  • Image classification<\/li>\n\n\n\n
                                  • Object detection<\/li>\n\n\n\n
                                  • Image segmentation<\/li>\n<\/ul>\n\n\n\n
                                    52. Write a Python program to convert a color image to grayscale using OpenCV.<\/strong><\/p>\n\n\n\n
                                    import cv2\nimage = cv2.imread('image.jpg')\ngray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\ncv2.imshow('Grayscale Image', gray_image)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
                                    53. What is the difference between grayscale and RGB images?<\/strong><\/p>\n\n\n\n
                                      \n
                                    • Grayscale:<\/strong> Contains shades of gray, using one channel.<\/li>\n\n\n\n
                                    • RGB:<\/strong> Contains colors represented by three channels (Red, Green, Blue).<\/li>\n<\/ul>\n\n\n\n
                                      54. Define feature extraction in computer vision.<\/strong>
                                      Feature extraction identifies important parts of an image (e.g., edges, corners) for analysis.<\/p>\n\n\n\n
                                      55. Explain the role of convolutional neural networks (CNNs) in computer vision.<\/strong>
                                      CNNs are deep learning models that process visual data, excelling in tasks like image recognition and object detection.<\/p>\n\n\n\n
                                      \n\n\n\n
                                      Natural Language Processing (NLP) (Additional Questions)<\/strong><\/h3>\n\n\n\n
                                      56. What is sentiment analysis in NLP?<\/strong>
                                      Sentiment analysis determines the sentiment (positive, negative, or neutral) expressed in a text.<\/p>\n\n\n\n
                                      57. Write a Python program to remove stopwords using NLTK.<\/strong><\/p>\n\n\n\n
                                      from nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\ntext = \"AI is changing the world rapidly.\"\nstop_words = set(stopwords.words('english'))\nwords = word_tokenize(text)\nfiltered_words = [w for w in words if w.lower() not in stop_words]\nprint(filtered_words)\n<\/code><\/pre>\n\n\n\n
                                      58. What are stopwords?<\/strong>
                                      Stopwords are common words (e.g., “and,” “the”) that are often removed from text data as they add little meaning.<\/p>\n\n\n\n
                                      59. What is text normalization?<\/strong>
                                      Text normalization converts text to a standard form, involving steps like lowercasing, removing punctuation, and stemming.<\/p>\n\n\n\n
                                      60. Explain the term “TF-IDF.”<\/strong>
                                      TF-IDF (Term Frequency-Inverse Document Frequency) measures the importance of a term in a document relative to a collection of documents.<\/p>\n\n\n\n
                                      \n\n\n\n
                                      Real-Life Applications of AI<\/strong><\/h3>\n\n\n\n
                                      61. How does AI help in healthcare?<\/strong>
                                      AI enables early diagnosis, personalized treatment, and predictive analytics in healthcare.<\/p>\n\n\n\n
                                      62. What is the role of AI in agriculture?<\/strong>
                                      AI helps optimize crop yields, detect diseases, and automate farming tasks using drones and sensors.<\/p>\n\n\n\n
                                      63. Name three AI-powered virtual assistants.<\/strong><\/p>\n\n\n\n
                                        \n
                                      • Siri<\/li>\n\n\n\n
                                      • Alexa<\/li>\n\n\n\n
                                      • Google Assistant<\/li>\n<\/ul>\n\n\n\n
                                        64. How does AI improve customer service?<\/strong>
                                        AI chatbots and sentiment analysis help provide faster, more personalized customer support.<\/p>\n\n\n\n
                                        65. Explain the role of AI in e-commerce.<\/strong>
                                        AI powers personalized product recommendations, inventory management, and fraud detection.<\/p>\n\n\n\n
                                        \n\n\n\n
                                        Model Evaluation (Additional Questions)<\/strong><\/h3>\n\n\n\n
                                        66. What is a True Positive (TP)?<\/strong>
                                        A TP occurs when the model correctly predicts a positive outcome.<\/p>\n\n\n\n
                                        67. What is a False Negative (FN)?<\/strong>
                                        An FN occurs when the model incorrectly predicts a negative outcome for a positive case.<\/p>\n\n\n\n
                                        68. Write a Python function to calculate accuracy from a confusion matrix.<\/strong><\/p>\n\n\n\n
                                        def calculate_accuracy(tp, tn, fp, fn):\n    total = tp + tn + fp + fn\n    return (tp + tn) \/ total\n<\/code><\/pre>\n\n\n\n
                                        69. Why is precision important in fraud detection?<\/strong>
                                        Precision ensures that flagged cases are truly fraudulent, minimizing false alarms.<\/p>\n\n\n\n
                                        70. What is the difference between Recall and Sensitivity?<\/strong>
                                        Recall and sensitivity both measure the ability to identify actual positives, but sensitivity is commonly used in medical diagnostics.<\/p>\n\n\n\n
                                        \n\n\n\n
                                        Chapter 7: Evaluation (Continued)<\/strong><\/h3>\n\n\n\n
                                        71. What is recall, and why is it important?<\/strong>
                                        Recall measures how well a model identifies all relevant instances. It is critical in cases like medical diagnosis, where missing a positive case can have severe consequences.<\/p>\n\n\n\n
                                        72. Write the formula to calculate recall.<\/strong>
                                        Recall=True PositivesTrue Positives+False Negatives\\text{Recall} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Negatives}}<\/p>\n\n\n\n
                                        73. What is the F1 score, and when should it be used?<\/strong>
                                        The F1 score is the harmonic mean of precision and recall. It is used when there is an imbalance between false positives and false negatives.<\/p>\n\n\n\n
                                        74. What is a confusion matrix, and how is it constructed?<\/strong>
                                        A confusion matrix is a table that summarizes the performance of a classification model by showing true positives, true negatives, false positives, and false negatives.<\/p>\n\n\n\n
                                        75. How is overfitting prevented in AI models?<\/strong>
                                        Overfitting can be prevented by:<\/p>\n\n\n\n
                                          \n
                                        • Using simpler models.<\/li>\n\n\n\n
                                        • Employing techniques like regularization.<\/li>\n\n\n\n
                                        • Using cross-validation during model training.<\/li>\n<\/ul>\n\n\n\n
                                          \n\n\n\n
                                          Advanced Python Programming (Additional Questions)<\/strong><\/h3>\n\n\n\n
                                          76. Write a Python program to find the largest number in a list.<\/strong><\/p>\n\n\n\n
                                          numbers = [10, 20, 30, 40, 50]\nlargest = max(numbers)\nprint(\"Largest number:\", largest)\n<\/code><\/pre>\n\n\n\n
                                          77. How do you install a Python library?<\/strong>
                                          Run the following command in your terminal:<\/p>\n\n\n\n
                                          pip install library_name\n<\/code><\/pre>\n\n\n\n
                                          78. What is the difference between a for<\/code> loop and a while<\/code> loop in Python?<\/strong><\/p>\n\n\n\n
                                            \n
                                          • For loop:<\/strong> Iterates over a sequence (e.g., list or range).<\/li>\n\n\n\n
                                          • While loop:<\/strong> Repeats as long as a condition is true.<\/li>\n<\/ul>\n\n\n\n
                                            79. Write a Python program to generate a bar chart using Matplotlib.<\/strong><\/p>\n\n\n\n
                                            import matplotlib.pyplot as plt\ncategories = ['A', 'B', 'C']\nvalues = [30, 40, 50]\nplt.bar(categories, values)\nplt.title(\"Bar Chart Example\")\nplt.show()\n<\/code><\/pre>\n\n\n\n
                                            80. What is the purpose of the pandas<\/code> library?<\/strong>
                                            Pandas is used for data manipulation and analysis, providing tools to work with structured data like DataFrames.<\/p>\n\n\n\n
                                            \n\n\n\n
                                            Data Science (Additional Questions)<\/strong><\/h3>\n\n\n\n
                                            81. What are the common types of data in AI?<\/strong><\/p>\n\n\n\n
                                              \n
                                            • Structured Data:<\/strong> Tabular format.<\/li>\n\n\n\n
                                            • Unstructured Data:<\/strong> Images, videos, text.<\/li>\n\n\n\n
                                            • Semi-Structured Data:<\/strong> JSON, XML files.<\/li>\n<\/ul>\n\n\n\n
                                              82. What is the importance of cleaning data in data science?<\/strong>
                                              Data cleaning ensures the dataset is free of errors, missing values, and inconsistencies, improving model accuracy.<\/p>\n\n\n\n
                                              83. Write a Python program to read a CSV file using Pandas.<\/strong><\/p>\n\n\n\n
                                              import pandas as pd\ndata = pd.read_csv('data.csv')\nprint(data.head())\n<\/code><\/pre>\n\n\n\n
                                              84. Define feature engineering.<\/strong>
                                              Feature engineering involves creating new features or modifying existing ones to improve a model’s performance.<\/p>\n\n\n\n
                                              85. What are the common statistical measures used in data science?<\/strong><\/p>\n\n\n\n
                                                \n
                                              • Mean<\/li>\n\n\n\n
                                              • Median<\/li>\n\n\n\n
                                              • Mode<\/li>\n\n\n\n
                                              • Standard Deviation<\/li>\n<\/ul>\n\n\n\n
                                                \n\n\n\n
                                                Computer Vision (Additional Questions)<\/strong><\/h3>\n\n\n\n
                                                86. What is the role of kernels in image processing?<\/strong>
                                                Kernels are small matrices used to apply transformations like edge detection or blurring in images.<\/p>\n\n\n\n
                                                87. Write a Python program to apply Gaussian blur to an image using OpenCV.<\/strong><\/p>\n\n\n\n
                                                import cv2\nimage = cv2.imread('image.jpg')\nblurred_image = cv2.GaussianBlur(image, (5, 5), 0)\ncv2.imshow('Blurred Image', blurred_image)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n<\/code><\/pre>\n\n\n\n
                                                88. What is the difference between edge detection and segmentation in CV?<\/strong><\/p>\n\n\n\n
                                                  \n
                                                • Edge Detection:<\/strong> Identifies boundaries in an image.<\/li>\n\n\n\n
                                                • Segmentation:<\/strong> Divides an image into meaningful regions.<\/li>\n<\/ul>\n\n\n\n
                                                  89. Explain the term “object detection.”<\/strong>
                                                  Object detection involves identifying and locating objects within an image or video.<\/p>\n\n\n\n
                                                  90. What is the significance of OpenCV in AI?<\/strong>
                                                  OpenCV is a widely used library for image processing and computer vision, enabling tasks like object detection, face recognition, and image manipulation.<\/p>\n\n\n\n
                                                  \n\n\n\n
                                                  Natural Language Processing (NLP) (Additional Questions)<\/strong><\/h3>\n\n\n\n
                                                  91. What are the major challenges in NLP?<\/strong><\/p>\n\n\n\n
                                                    \n
                                                  • Ambiguity in language.<\/li>\n\n\n\n
                                                  • Understanding context.<\/li>\n\n\n\n
                                                  • Handling unstructured data.<\/li>\n<\/ul>\n\n\n\n
                                                    92. Write a Python program to count the frequency of words in a text.<\/strong><\/p>\n\n\n\n
                                                    from collections import Counter\ntext = \"AI is transforming the world. AI is everywhere.\"\nword_count = Counter(text.split())\nprint(word_count)\n<\/code><\/pre>\n\n\n\n
                                                    93. What is lemmatization in NLP?<\/strong>
                                                    Lemmatization reduces words to their root forms, considering the context (e.g., “running” \u2192 “run”).<\/p>\n\n\n\n
                                                    94. Explain the term “language model.”<\/strong>
                                                    A language model predicts the likelihood of a sequence of words, helping in tasks like text generation and translation.<\/p>\n\n\n\n
                                                    95. What is the purpose of text vectorization in NLP?<\/strong>
                                                    Text vectorization converts text into numerical data for machine learning algorithms to process.<\/p>\n\n\n\n
                                                    \n\n\n\n
                                                    Real-Life Applications of AI (Additional Questions)<\/strong><\/h3>\n\n\n\n
                                                    96. How does AI contribute to education?<\/strong>
                                                    AI personalizes learning, automates administrative tasks, and enables intelligent tutoring systems.<\/p>\n\n\n\n
                                                    97. What is the role of AI in transportation?<\/strong>
                                                    AI powers self-driving cars, optimizes traffic management, and improves logistics.<\/p>\n\n\n\n
                                                    98. How is AI used in financial services?<\/strong>
                                                    AI detects fraud, predicts stock trends, and provides personalized financial advice.<\/p>\n\n\n\n
                                                    99. What are AI’s contributions to environmental conservation?<\/strong>
                                                    AI monitors wildlife, predicts natural disasters, and optimizes energy usage.<\/p>\n\n\n\n
                                                    100. Explain how AI is transforming the entertainment industry.<\/strong>
                                                    AI powers content recommendations (e.g., Netflix), enhances visual effects, and creates virtual actors.<\/p>\n\n\n\n
                                                    \n\n\n\n
                                                    Conclusion<\/strong><\/h3>\n\n\n\n
                                                    These 100 questions and answers provide comprehensive preparation for your CBSE Class X Artificial Intelligence (AI) (417) board exams. Focus on understanding the concepts, practicing Python coding, and exploring real-life AI applications.<\/p>\n","protected":false},"excerpt":{"rendered":"
                                                    Artificial Intelligence (AI) is a crucial subject for CBSE Class X students, focusing on real-world applications, ethics, and programming skills. To help you excel in your AI (417) board exams, here are 100 chapter-wise important questions and answers organized in blog post format. These cover key topics from the curriculum, including theory, practical, and project-related […]<\/p>\n","protected":false},"author":1,"featured_media":913,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"googlesitekit_rrm_CAow44u0DA:productID":"","footnotes":""},"categories":[169],"tags":[],"class_list":["post-912","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-viva"],"acf":[],"yoast_head":"\n100 Important Questions and Answers for CBSE Class X Artificial Intelligence (AI-417) Board Exam - Itxperts<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/itxperts.co.in\/blog\/100-important-questions-and-answers-for-cbse-class-x-artificial-intelligence-ai-code-417-board-exam\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"100 Important Questions and Answers for CBSE Class X Artificial Intelligence (AI-417) Board Exam - Itxperts\" \/>\n<meta property=\"og:description\" content=\"Artificial Intelligence (AI) is a crucial subject for CBSE Class X students, focusing on real-world applications, ethics, and programming skills. 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