Generative AI Class 9 MCQ

Generative AI Class 9 MCQ – The CBSE has changed the previous textbook and the syllabus of Std. IX. The new notes are made based on the new syllabus and based on the New CBSE textbook.  All the important Information are taken from the Artificial Intelligence Class IX Textbook Based on CBSE Board Pattern.

Generative AI Class 9 MCQ

Generative AI Class 9 MCQ

1. What is generative AI?
a. AI can generate new data.
b. AI can analyze existing data
c. AI can optimize data
d. None of the above

Show Answer ⟶
a. AI can generate new data

2. How does generative AI work?
a. Making predictions
b. Mimic human behavior
c. Learn from patterns
d. All of the above

Show Answer ⟶
c. Learn from patterns

3. What is the purpose of the discriminator network in GANs?
a. Produces the data
b. Analyzes the data and provides feedback
c. Handel the sequential data
d. All of the above

Show Answer ⟶
b. Analyses the data and provides feedback

4. Which of the following is not considered a type of generative AI?
a. Convolutional Neural Networks (CNNs)
b. Generative Adversarial Networks (GANs)
c. Recurrent Neural Networks (RNNs)
d. Auto Encoder

Show Answer ⟶
a. Convolutional Neural Networks (CNNs)

5. How is the generative AI used in the application?
a. Generate a realistic image
b. To improve user application
c. Automate repetitive task
d. None of the above

Show Answer ⟶
a. Generate a realistic image.

6. What are Generative Adversarial Networks?
a. It is used for the distribution of the data
b. It produces fresh data
c. Handle sequential data
d. None of the above

Show Answer ⟶
b. It produces fresh data

7. Examples of Generative Adversarial Networks?
a. Convert images from day to night
b. Generate images based on textual description
c. Generate realistic video
d. All of the above

Show Answer ⟶
d. All of the above

8. Which of the following neural networks learns the distribution of the data and then samples from it?
a. Generative Adversarial Network (GANs)
b. Recurrent Neural Networks (RNNs)
c. Variational Autoencoders (VAEs)
d. Autoencoder

Show Answer ⟶
c. Variational Autoencoders (VAEs)

9. Which of the following neural networks is used to produce the data?
a. Discriminator network
b. Generator network
c. Both a) and b)
d. None of the above

Show Answer ⟶
b. Generator network

10. Neural networks are made up of _.
a. Generator Network
b. Discriminator Network
c. Both a) and b)
d. None of the above

Show Answer ⟶
c. Both a) and b)

11. Example of a recurrent neural network?
a. Generating novel text
b. Predicting the next character
c. Predicting the next word in a sequence
d. All of the above

Show Answer ⟶
d. All of the above

12. What are the common applications of generative AI?
a. Image Generation
b. Text Generation
c. Video Generation
d. All of the above

Show Answer ⟶
d. All of the above

13. Examples of variational autoencoders?
a. Generation of new images similar to the given training set
b. Image reconstruction
c. Generating drafts for the writer
d. All of the above

Show Answer ⟶
d. All of the above

14. Which neural network is helpful for handling sequential data?
a. Generative Adversarial Network (GANs)
b. Recurrent Neural Networks (RNNs)
c. Variational Autoencoders (VAEs)
d. Autoencoder

Show Answer ⟶
b. Recurrent Neural Network (RNNs)

15. Which neural network can learn a compressed representation of data?
a. Generative Adversarial Network (GANs)
b. Recurrent Neural Networks (RNNs)
c. Variational Autoencoders (VAEs)
d. Autoencoder

Show Answer ⟶
d. Autoencoder

16. Autoencoders can be applied in _.
a. Denoising
b. Picture compression application
c. Both a) and b)
d. None of the above

Show Answer ⟶
c. Both a) and b)

17. Examples of autoencoders?
a. They generate highly realistic image
b. Artistic image creation
c. Drug discovery
d. All of the above

Show Answer ⟶
d. All of the above

18. What is supervised learning and discriminative modeling?
a. Initially taught to the machine models by humans
b. Analyze unlabeled data and discover patterns without human
c. Both a) and b)
d. None of the above

Show Answer ⟶
a. Initially taught to the machine models by humans

19. What are unsupervised learning and generative modeling?
a. Initially taught to the machine models by humans
b. Analyze unlabeled data and discover patterns without human
c. Both a) and b)
d. None of the above

Show Answer ⟶
b. Analyze unlabeled data and discover patterns without human

20. Generative AI refers to the algorithms that generate __.
a. Generate new and unique audio sound
b. Generate new and unique Images
c. Simulations and generate new and unique videos
d. All of the above

Show Answer ⟶
d. All of the above

21. Which type of content does generative AI produce?
a. Fresh
b. Innovative
c. Often unexpected
d. All of the above

Show Answer ⟶
d. All of the above

22. In which of the following fields is conventional AI commonly used?
a. Banking and healthcare
b. Art and Music
c. Design and Movie Production
d. None of the above

Show Answer ⟶
a. Banking and healthcare

23. Which type of content does conventional AI produce?
a. Fresh and innovative
b. Predictable and based on existing data
c. Based on new patterns
d. None of the above

Show Answer ⟶
b. Predictable and based on existing data

24. Which of the following limitations belong to generative AI?
a. If generative AI is trained on incomplete data, then the output may be incomplete.
b. Generative AI can produce unexpected results, which may be a drawback.
c. Generative AI requires significant computational resources to train the model.
d. All of the above

Show Answer ⟶
d. All of the above

25. Generative AI can be helpful in which of the following fields?
a. Coding and image generation
b. Music
c. Content creation
d. All of the above

Show Answer ⟶
All of the above

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Generative AI Class 9 MCQ

Generative AI Class 9 MCQ

Generative AI Class 9 MCQ

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