Students can practice the best AI Class 9 MCQ Chapter 4 Introduction to Generative AI Class 9 MCQ with Answers for exam preparation.
Class 9 Introduction to Generative AI MCQ
MCQ on Introduction to Generative AI Class 9
Class 9 Introduction to Generative AI MCQ – Introduction to Generative AI MCQ Class 9
Multiple Choice Questions
Question 1.
Application of generative AI are:
(a) Music
(b) Art
(c) 3 D printing
(d) All of the above
Answer:
(d) All of the above
Question 2.
Which of these is not a limitation of generative AI are:
(a) Accessibility
(b)Uncertainty
(c) Data bias
(d) Computational demands
Answer:
(a) Accessibility
Question 3.
What is the primary function of generative AI?
(a) To classify data
(b) To predict future events
(c) To generate new content similar to the training data
(d) To sort data into categories
Answer:
(c) To generate new content similar to the training data
Question 4.
Which of the following architectures is commonly used for generative text models?
(a) Convolutional Neural Network (CNN)
(b) Recurrent Neural Network (RNN)
(c) Decision Tree
(d) Support Vector Machine (SVM)
Answer:
(b) Recurrent Neural Network (RNN)
Question 5.
In the context of generative AI, what does the term “tokenizing” refer to?
(a) Compressing the dataset
(b) Breaking down text into smaller units like words or phrases
(c) Encrypting data for security
(d) Sorting data into categories
Answer:
(b) Breaking down text into smaller units like words or phrases
Question 6.
Which of the following is a potential ethical issue with generative AI?
(a) Increased processing speed
(b) Enhanced data storage
(c) Plagiarism and copyright concerns
(d) Improved accuracy of predictions
Answer:
(c) Plagiarism and copyright concerns
Question 7.
What is the purpose of the preprocessing step in building a generative AI model?
(a) To visualize the data
(b) To clean and prepare the data for training
(c) To encrypt the data
(d) To increase the size of the dataset
Answer:
(b) To clean and prepare the data for training
Question 8.
Why is it important to evaluate the originality of content generated by a generative AI model?
(a) To ensure the AI is working faster
(b) To verify the AI’s processing power
(c) To avoid plagiarism and maintain the uniqueness of the content
(d) To check the AI’s data storage capabilities
Answer:
(c) To avoid plagiarism and maintain the uniqueness of the content
Question 9.
Which statistical measure is crucial for understanding the variability in a dataset used for generative AI?
(a) Mean
(b) Median
(c) Mode
(d) Standard Deviation
Answer:
(d) Standard Deviation
Question 10.
How does an RNN handle sequential data in generative AI tasks?
(a) By processing each data point independently
(b) By maintaining a memory of previous inputs to understand context
(c) By randomly generating outputs
(d) By sorting the data alphabetically
Answer:
(b) By maintaining a memory of previous inputs to understand context
Question 11.
What is one way to ensure the data used for training a generative AI model is of high quality?
(a) By using only numerical data
(b) By cleaning and normalizing the data
(c) By minimizing the dataset size
(d) By using encrypted data
Answer:
(b) By cleaning and normalizing the data
Question 12.
Which of the following best describes the concept of conditional probability in generative AI?
(a) The likelihood of an event occurring regardless of other events
(b) The likelihood of an event occurring given that another event has occurred
(c) The process of averaging all possible outcomes
(d) The measure of how often an event occurs in a dataset
Answer:
(b) The likelihood of an event occurring given that another event has occurred
Assertion-reasoning based questions
Study the two statements labeled as assertion (a) and reason (r).
Point out if:
(a) Bqth, a and r, are true and r is the correct explanation of a
(b) Both, a and r, are true but r is not the correct explanation of a
(c) If a is true but r is false
(d) If a is false but r is true
Question 1.
Assertion: Generative Adversarial Networks (GANs) consist of two neural networks, a generator and a discriminator, that compete against each other.
Reason: The generator creates fake data while the discriminator attempts to distinguish between real and fake data.
Answer:
(a) Bqth, a and r, are true and r is the correct explanation of a
Question 2.
Assertion: Variational Autoencoders (VAEs) are designed to generate new data points that are similar to the training data.
Reason: VAEs work by learning the distribution of the training data and sampling from this distribution to generate new data.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a
Question 3.
Assertion: Generative AI models can only be trained using supervised learning techniques.
Reason: Supervised learning requires labeled data, which is essential for training generative models to create realistic outputs.
Answer:
(d) If a is false but r is true
Question 4.
Assertion: One advantage of generative AI models is their ability to create new content, such as images, text, and music.
Reason: Generative AI models learn the underlying pattern’s and structures of the training data and use this knowledge to generate novel outputs.
Answer:
(a) Bqth, a and r, are true and r is the correct explanation of a
Question 5.
Assertion: Generative AI can be used to enhance data privacy by generating synthetic data that mimics real data.
Reason: Synthetic data generated by generative AI can be used to train models without exposing sensitive real data.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a