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 queries.
Chapter 1: Introduction to Artificial Intelligence (AI)
1. What is Artificial Intelligence (AI)?
AI refers to the simulation of human intelligence in machines that are programmed to think, learn, and solve problems like humans.
2. Name the three main domains of AI.
- Data Science
- Computer Vision
- Natural Language Processing (NLP)
3. List some real-life applications of AI.
- Virtual assistants like Siri and Alexa
- Self-driving cars
- Personalized recommendations on platforms like Netflix
4. What are AI ethics?
AI ethics deal with the moral principles guiding the development and use of AI, such as fairness, transparency, and privacy.
5. How does AI help achieve Sustainable Development Goals (SDGs)?
AI aids in addressing global challenges, such as improving healthcare, optimizing agriculture, and combating climate change.
Chapter 2: AI Project Cycle
6. What are the stages of the AI Project Cycle?
- Problem Scoping
- Data Acquisition
- Data Exploration
- Modeling
- Evaluation
7. What is the importance of problem scoping in an AI project?
Problem scoping defines the project goals, objectives, and constraints, ensuring a clear direction for the AI model.
8. Explain the term “Data Acquisition.”
Data acquisition is the process of collecting relevant and reliable data for training and testing an AI model.
9. What is data visualization? Why is it important?
Data visualization involves graphical representation of data. It helps in identifying patterns, trends, and insights for decision-making.
10. What does “Evaluation” mean in the AI Project Cycle?
Evaluation assesses the performance of an AI model using metrics like accuracy, precision, recall, and F1 score.
Chapter 3: Advanced Python
11. Define a variable in Python.
A variable is a container for storing data values in a program.
12. What is the difference between a list and a tuple in Python?
- List: Mutable (can be changed), e.g.,
[1, 2, 3] - Tuple: Immutable (cannot be changed), e.g.,
(1, 2, 3)
13. Write a Python program to calculate the sum of two numbers.
a = 5
b = 10
print("Sum:", a + b)
14. Name three Python libraries commonly used in AI.
- NumPy
- Pandas
- Matplotlib
15. What is the purpose of Jupyter Notebook?
Jupyter Notebook is an open-source tool for writing, testing, and sharing Python code in an interactive format.
Chapter 4: Data Science
16. Define data science.
Data science is the study of data to extract meaningful insights using techniques like analysis, visualization, and modeling.
17. What is NumPy used for in Python?
NumPy is used for numerical computations, such as array operations and mathematical functions.
18. Write a Python program to calculate the mean of a dataset.
import numpy as np
data = [10, 20, 30, 40]
mean = np.mean(data)
print("Mean:", mean)
19. Explain the term “data exploration.”
Data exploration involves examining data sets to summarize their characteristics, often using statistical tools.
20. What are the common types of graphs used in data visualization?
- Line chart
- Bar graph
- Scatter plot
Chapter 5: Computer Vision
21. What is Computer Vision (CV)?
CV is a field of AI that enables machines to interpret and analyze visual information from images or videos.
22. Explain the term “pixel.”
A pixel is the smallest unit of a digital image, representing a single point of color.
23. What is the purpose of OpenCV in AI?
OpenCV is an open-source library used for image processing and computer vision tasks.
24. Write a Python program to read and display an image using OpenCV.
import cv2
image = cv2.imread('image.jpg')
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
25. What are RGB images?
RGB images use three color channels—Red, Green, and Blue—to represent colors in a digital image.
Chapter 6: Natural Language Processing (NLP)
26. Define Natural Language Processing.
NLP is a field of AI that enables machines to understand, interpret, and generate human language.
27. What is tokenization in NLP?
Tokenization is the process of breaking a text into smaller units like words or sentences.
28. Write a Python program to tokenize a sentence using NLTK.
from nltk.tokenize import word_tokenize
sentence = "AI is transforming the world."
tokens = word_tokenize(sentence)
print(tokens)
29. Explain the Bag-of-Words model in NLP.
The Bag-of-Words model represents text data as a collection of words and their frequency, ignoring grammar and word order.
30. List some applications of NLP in daily life.
- Chatbots
- Sentiment analysis
- Translation tools
Chapter 7: Evaluation
31. What is model evaluation?
Model evaluation measures the performance of an AI model using specific metrics.
32. Define accuracy, precision, recall, and F1 score.
- Accuracy: Percentage of correct predictions.
- Precision: Ratio of true positives to all predicted positives.
- Recall: Ratio of true positives to all actual positives.
- F1 Score: Harmonic mean of precision and recall.
33. Write an example of a confusion matrix.
| Predicted | Positive | Negative |
|---|---|---|
| Positive | True Positive | False Negative |
| Negative | False Positive | True Negative |
34. Why is a confusion matrix important?
It provides a detailed breakdown of model predictions, helping to identify errors and areas for improvement.
35. What is underfitting and overfitting?
- Underfitting: Model is too simple and performs poorly.
- Overfitting: Model is too complex and performs well on training data but poorly on new data.
More Practice Questions by Topic
Problem-Solving in AI
- What is supervised learning?
- Explain reinforcement learning with an example.
Programming with Python
- Write a program to calculate the median using NumPy.
- How do you create a scatter plot in Matplotlib?
Real-Life Applications
- How does AI contribute to healthcare?
- What role does AI play in climate change solutions?
Chapter 7: Evaluation (Continued)
36. Why is the F1 score important in evaluating AI models?
The F1 score balances precision and recall, making it useful when dealing with imbalanced datasets.
37. What is the difference between validation and testing in model evaluation?
- Validation: Used during model training to tune parameters.
- Testing: Used after training to measure the model’s final performance.
38. Write a formula to calculate precision.
Precision=True PositivesTrue Positives + False Positives\text{Precision} = \frac{\text{True Positives}}{\text{True Positives + False Positives}}
39. What is the role of a confusion matrix?
A confusion matrix evaluates a model by showing the number of true/false positives and true/false negatives.
40. How do you determine if a model is overfitting?
If a model performs well on training data but poorly on validation or test data, it is overfitting.
Advanced Python Programming (Additional Questions)
41. How do you create a virtual environment in Python?
Run the following command:
python -m venv env_name
42. What is the purpose of the matplotlib library?
Matplotlib is used for creating static, interactive, and animated visualizations in Python.
43. Write a Python program to create a line graph using Matplotlib.
import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
plt.plot(x, y)
plt.title("Line Graph Example")
plt.show()
44. How do you import the Pandas library in Python?
Use the command:
import pandas as pd
45. What is the difference between df.head() and df.tail() in Pandas?
df.head(): Displays the first 5 rows of a DataFrame.df.tail(): Displays the last 5 rows of a DataFrame.
Data Science (Additional Questions)
46. What is the difference between structured and unstructured data?
- Structured Data: Organized in a fixed format, like rows and columns (e.g., databases).
- Unstructured Data: Does not follow a specific format (e.g., images, videos).
47. Write a Python program to calculate the standard deviation of a dataset.
import numpy as np
data = [10, 20, 30, 40]
std_dev = np.std(data)
print("Standard Deviation:", std_dev)
48. What is a CSV file, and why is it important in data science?
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.
49. What are the key steps in data preprocessing?
- Cleaning
- Normalization
- Transformation
- Feature selection
50. Define outlier detection in data analysis.
Outlier detection involves identifying data points that differ significantly from the majority of the dataset.
Computer Vision (Additional Questions)
51. What are the basic tasks in computer vision?
- Image classification
- Object detection
- Image segmentation
52. Write a Python program to convert a color image to grayscale using OpenCV.
import cv2
image = cv2.imread('image.jpg')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('Grayscale Image', gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
53. What is the difference between grayscale and RGB images?
- Grayscale: Contains shades of gray, using one channel.
- RGB: Contains colors represented by three channels (Red, Green, Blue).
54. Define feature extraction in computer vision.
Feature extraction identifies important parts of an image (e.g., edges, corners) for analysis.
55. Explain the role of convolutional neural networks (CNNs) in computer vision.
CNNs are deep learning models that process visual data, excelling in tasks like image recognition and object detection.
Natural Language Processing (NLP) (Additional Questions)
56. What is sentiment analysis in NLP?
Sentiment analysis determines the sentiment (positive, negative, or neutral) expressed in a text.
57. Write a Python program to remove stopwords using NLTK.
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
text = "AI is changing the world rapidly."
stop_words = set(stopwords.words('english'))
words = word_tokenize(text)
filtered_words = [w for w in words if w.lower() not in stop_words]
print(filtered_words)
58. What are stopwords?
Stopwords are common words (e.g., “and,” “the”) that are often removed from text data as they add little meaning.
59. What is text normalization?
Text normalization converts text to a standard form, involving steps like lowercasing, removing punctuation, and stemming.
60. Explain the term “TF-IDF.”
TF-IDF (Term Frequency-Inverse Document Frequency) measures the importance of a term in a document relative to a collection of documents.
Real-Life Applications of AI
61. How does AI help in healthcare?
AI enables early diagnosis, personalized treatment, and predictive analytics in healthcare.
62. What is the role of AI in agriculture?
AI helps optimize crop yields, detect diseases, and automate farming tasks using drones and sensors.
63. Name three AI-powered virtual assistants.
- Siri
- Alexa
- Google Assistant
64. How does AI improve customer service?
AI chatbots and sentiment analysis help provide faster, more personalized customer support.
65. Explain the role of AI in e-commerce.
AI powers personalized product recommendations, inventory management, and fraud detection.
Model Evaluation (Additional Questions)
66. What is a True Positive (TP)?
A TP occurs when the model correctly predicts a positive outcome.
67. What is a False Negative (FN)?
An FN occurs when the model incorrectly predicts a negative outcome for a positive case.
68. Write a Python function to calculate accuracy from a confusion matrix.
def calculate_accuracy(tp, tn, fp, fn):
total = tp + tn + fp + fn
return (tp + tn) / total
69. Why is precision important in fraud detection?
Precision ensures that flagged cases are truly fraudulent, minimizing false alarms.
70. What is the difference between Recall and Sensitivity?
Recall and sensitivity both measure the ability to identify actual positives, but sensitivity is commonly used in medical diagnostics.
Chapter 7: Evaluation (Continued)
71. What is recall, and why is it important?
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.
72. Write the formula to calculate recall.
Recall=True PositivesTrue Positives+False Negatives\text{Recall} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}}
73. What is the F1 score, and when should it be used?
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.
74. What is a confusion matrix, and how is it constructed?
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.
75. How is overfitting prevented in AI models?
Overfitting can be prevented by:
- Using simpler models.
- Employing techniques like regularization.
- Using cross-validation during model training.
Advanced Python Programming (Additional Questions)
76. Write a Python program to find the largest number in a list.
numbers = [10, 20, 30, 40, 50]
largest = max(numbers)
print("Largest number:", largest)
77. How do you install a Python library?
Run the following command in your terminal:
pip install library_name
78. What is the difference between a for loop and a while loop in Python?
- For loop: Iterates over a sequence (e.g., list or range).
- While loop: Repeats as long as a condition is true.
79. Write a Python program to generate a bar chart using Matplotlib.
import matplotlib.pyplot as plt
categories = ['A', 'B', 'C']
values = [30, 40, 50]
plt.bar(categories, values)
plt.title("Bar Chart Example")
plt.show()
80. What is the purpose of the pandas library?
Pandas is used for data manipulation and analysis, providing tools to work with structured data like DataFrames.
Data Science (Additional Questions)
81. What are the common types of data in AI?
- Structured Data: Tabular format.
- Unstructured Data: Images, videos, text.
- Semi-Structured Data: JSON, XML files.
82. What is the importance of cleaning data in data science?
Data cleaning ensures the dataset is free of errors, missing values, and inconsistencies, improving model accuracy.
83. Write a Python program to read a CSV file using Pandas.
import pandas as pd
data = pd.read_csv('data.csv')
print(data.head())
84. Define feature engineering.
Feature engineering involves creating new features or modifying existing ones to improve a model’s performance.
85. What are the common statistical measures used in data science?
- Mean
- Median
- Mode
- Standard Deviation
Computer Vision (Additional Questions)
86. What is the role of kernels in image processing?
Kernels are small matrices used to apply transformations like edge detection or blurring in images.
87. Write a Python program to apply Gaussian blur to an image using OpenCV.
import cv2
image = cv2.imread('image.jpg')
blurred_image = cv2.GaussianBlur(image, (5, 5), 0)
cv2.imshow('Blurred Image', blurred_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
88. What is the difference between edge detection and segmentation in CV?
- Edge Detection: Identifies boundaries in an image.
- Segmentation: Divides an image into meaningful regions.
89. Explain the term “object detection.”
Object detection involves identifying and locating objects within an image or video.
90. What is the significance of OpenCV in AI?
OpenCV is a widely used library for image processing and computer vision, enabling tasks like object detection, face recognition, and image manipulation.
Natural Language Processing (NLP) (Additional Questions)
91. What are the major challenges in NLP?
- Ambiguity in language.
- Understanding context.
- Handling unstructured data.
92. Write a Python program to count the frequency of words in a text.
from collections import Counter
text = "AI is transforming the world. AI is everywhere."
word_count = Counter(text.split())
print(word_count)
93. What is lemmatization in NLP?
Lemmatization reduces words to their root forms, considering the context (e.g., “running” → “run”).
94. Explain the term “language model.”
A language model predicts the likelihood of a sequence of words, helping in tasks like text generation and translation.
95. What is the purpose of text vectorization in NLP?
Text vectorization converts text into numerical data for machine learning algorithms to process.
Real-Life Applications of AI (Additional Questions)
96. How does AI contribute to education?
AI personalizes learning, automates administrative tasks, and enables intelligent tutoring systems.
97. What is the role of AI in transportation?
AI powers self-driving cars, optimizes traffic management, and improves logistics.
98. How is AI used in financial services?
AI detects fraud, predicts stock trends, and provides personalized financial advice.
99. What are AI’s contributions to environmental conservation?
AI monitors wildlife, predicts natural disasters, and optimizes energy usage.
100. Explain how AI is transforming the entertainment industry.
AI powers content recommendations (e.g., Netflix), enhances visual effects, and creates virtual actors.
Conclusion
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.
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