Generative AI MCQs (Multiple-Choice Questions)

1. What is Generative AI?

1. AI to analyze data and reports
2. AI which can generate new content
3. AI to perform repeated tasks
4. AI for voice detection

Answer: B) AI which can generate new content

Explanation:

Generative AI generates new text, images, audios, videos etc. on request.

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2. Generative AI is used for ____.

1. Generating new data
2. Categorizing the data
3. Analyzing the data
4. All of the above

Answer: A) Generating new data

Explanation:

Generative AI is used for generating new data based on the given user input (prompt), Generative AI refers to algorithms like GANs or VAEs.

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3. What is/are the commonly used models in Generative AI?

1. Convolutional Neural Network (CNN)
2. Long Short-Term Memory (LSTM)
3. Generative Adversarial Network (GAN)
4. Recurrent Neural Network (RNN)

Answer: C) Generative Adversarial Network (GAN)

Explanation:

Generative Adversarial Network (GAN) is a commonly used model in Generative AI. The Generative Adversarial Networks (GANs) were designed by Ian Good fellow and his colleagues in 2014.

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4. Which kind of data can be generated using Generative AI?

1. Text
2. Images
3. Audios and Videos
4. All of the above

Answer: C) Audios and Videos

Explanation:

Generative AI can be used to generate (create) the following type of data:

• Text
• Images
• Audios
• Videos
• 3D Models

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5. AI prompt writing (engineering) is the ____.

1. Process of writing input to instruct the Generative AI to generate data
2. Response returned by the Generative AI
3. Confirmation of the response
4. All of the above

Answer: A) Process of writing input to instruct the Generative AI to generate data

Explanation:

AI prompt writing (engineering) is the process of writing input to instruct the Generative AI to generate data.

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6. Generative AI tools use ____ to create text, images, audios, videos, and more.

1. Python
2. Artificial Intelligence
3. Deep Learning
4. None of the above

Answer: B) Artificial Intelligence

Explanation:

Generative AI tools use artificial intelligence to create text, images, audios, videos, and more.

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7. Is ChatGPT a generative AI tool?

1. Yes
2. No
3. Can't say
4. None of the above

Answer: A) Yes

Explanation:

Yes! ChatGPT is a generative AI tool. The ChatGPT is a generative AI chatbot that generates text, images, and more based on the given prompt.

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8. Different types of prompts in Prompt Writing (Engineering) are ____.

1. Instructions and Questions
2. Data and Examples
3. Both A and B
4. None of the above

Answer: C) Both A and B

Explanation:

Different types of prompts in Prompt Writing (Engineering) are as follows:

• Instructions
• Questions
• Data
• Examples

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9. Text-to-text Generative AI refers to ____.

1. Generate text based on the given text
2. Generate text based on the given audio
3. Generate text based on the given pdf
4. Generate text based on the given any kind of prompt

Answer: A) Generate text based on the given text

Explanation:

Text-to-text Generative AI refers to generating text based on any kind of prompt.

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10. What is Generative Adversarial Network (GAN)?

1. Data analysis network
2. Data generative network
3. Neural network for generative modelling
4. Data encryption network

Answer: C) Neural network for generative modelling

Explanation:

Generative Adversarial Network (GAN) is a powerful class of machine learning models. A GAN consists of two neural networks, generator and discriminator where each network competes with each other to improve their performance.

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11. What is the role of the discriminator in a GAN model?

1. Generating fake data
2. Optimizing the prompt data
3. Analyzing the prompt data
4. Differentiating real vs. fake data

Answer: D) Differentiating real vs. fake data

Explanation:

In a GAN, the role of the discriminator is to differentiate between real and fake data.

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12. Discriminator in Generative Adversarial Network (GAN) is ____?

1. To generate new data
2. To validate and classify data as real or generated
3. To store data
4. To perform optimization

Answer: B) To validate and classify data as real or generated

Explanation:

Discriminator is a neural network of Generative Adversarial Network (GAN) to classify data of a dataset as real or fake.

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13. Generator in Generative Adversarial Network (GAN) is ____?

1. To generate new data resembling training data
2. To validate and classify data as real or generated
3. To store data
4. To perform optimization

Answer: B) To validate and classify data as real or generated

Explanation:

Generator is a neural network of Generative Adversarial Network (GAN) to create new data samples which resembles the training data.

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14. Applications of GANs?

1. Image Generation: Generate high quality images of faces, objects and scenes.
2. Style Transfer: Copy style of one image onto another image, creating artistic effects.
3. Super Resolution: Enhance resolution of low-resolution images
4. All of these

Answer: D) All of these

Explanation:

GANs can create high quality images, can create artistic effects by copying styles, can enhance low resolution images and lots more.

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15. DeepFake technology is ____?

1. A data encryption methodology
2. To use AI to create fake audio and video content
3. A hacking technique
4. A data analysis technique

Answer: B) To use AI to create fake audio and video content

Explanation:

DeepFake technology is to use Artificial Intelligence, especially deep learning to fake an audio or video where a person is morphed to say something else instead of the original statement.

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16. 'Text-To-Image' in the context of Generative AI is ____?

1. To converting text data to Image
2. To use AI to create images from text-based description
3. To convert Images to text
4. All of these

Answer: B) To use AI to create images from text-based description

Explanation:

In 'Text-To-Image' technology, Generative AI can generate images from text-based descriptions using AI algorithms with the help of neural networks. Generative AI understands the text and creates an image accordingly.

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17. Transformer Model in Generative AI is ____?

1. A model used for image transformation
2. To validate and classify data as real or generated
3. To store data
4. A type of neural network architecture to capture long range dependencies in sequential data

Answer: D) A type of neural network architecture to capture long range dependencies in sequential data

Explanation:

Transformer is a powerful tool in Generative AI in natural language processing. A transformer model is able to capture long range dependencies in sequential data.

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18. Style Transfer in Generative AI is ____?

1. To format a document
2. To copy the style of an image onto another image
3. To visualize data
4. A type of neural network architecture to copy data

Answer: B) To copy the style of an image onto another image

Explanation:

Style Transfer is a technique in Generative AI to copy the style of an image like the artistic style of a painting and apply it on another image to create a new artistic image.

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19. Latent Space in Generative AI is ____?

1. To store data
2. An intermediate representation of data used by Generative AI for learning and prepare its model
3. A virtual storage space
4. A temporary space for data transmission

Answer: B) An intermediate representation of data used by Generative AI for learning and prepare its model

Explanation:

Latent Space represents an intermediate representation of data used by Generative AI. It is a compressed representation of knowledge having similar data points together in space.

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20. AutoEncoder in Generative AI mainly ____?

1. Encrypts data
2. Automatically encodes the data
3. Used for dimensionality reduction and generative modelling
4. Improves performance

Answer: C) Used for dimensionality reduction and generative modelling

Explanation:

AutoEncoders in Generative AI are a type of artificial neural network primarily used for dimensionality reduction and generative modelling. Autoencoders can be used to learn efficient representation of data and to produce new sample data resembling the original data.

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21. AutoEncoder in Generative AI has ___?

1. Encoder Component
2. Decoder Component
3. Both of the above
4. None of the above

Answer: C) Both of the above

Explanation:

Autoencoder consists of two main components:

1. Encoder: It takes an input and compresses it into a lower-dimensional representation as a latent code.
2. Decoder: It takes the latent code and reconstructs the original input.

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22. 'Neural Style Transfer' in Generative AI is to?

1. Data transfer
2. Secure network
3. Apply style of one image to another image using neural network
4. Compress data

Answer: C) Apply style of one image to another image using neural network

Explanation:

Neural style transfer is an excellent technique to apply the artistic style of an image onto the content of one image and as a result, create visually stunning and unique results. It is similar to painting a photograph by a computer using deep learning using the style of a famous artist.

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23. 'Neural Style Transfer' in Generative AI can be used for ___?

1. Personalized Artistic expression
2. Enhance movie scenes with artistic styles
3. Apply artistic filters on photographs
4. All of these

Answer: D) All of these

Explanation:

Neural style transfer is an excellent technique to apply the artistic style of an image onto the content of one image and as a result, create visually stunning and unique results. It can be used to create personalized artistic images, enhance movie scenes with artistic style and apply artistic filters on photographs.

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24. 'Variational AutoEncoder' in Generative AI is ____?

1. Visual data modeler
2. Data compression autoencoder
3. A generative model which uses probabilistic encoding
4. None of the above

Answer: C) A generative model which uses probabilistic encoding

Explanation:

Variational AutoEncoder is a type of generative model that applies a probabilistic twist to the encoding process. Instead of producing a single latent code, a variational autoencoder learns a probability distribution over multiple possible latent codes. This approach permits VAE to generate new data samples by sampling from this distribution and then decoding the sampled latent code.

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25. 'Tokenization' in Natural Language Processing helps ____?

1. In encoding the data
2. In creating token for transfer over network
3. Breaking down text into smaller units for processing
4. None of the above

Answer: C) Breaking down text into smaller units for processing

Explanation:

'Tokenization' in Natural Language Processing helps in breaking down text into smaller units like words, phrases. It helps generative models to understand and process text efficiently.

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