Gen AI Basics Quiz 2 > Data AI Revolution

Gen AI Basics Quiz 2

This is the second quiz in the Generative AI Basics series, featuring new questions that cover key concepts such as GANs, Variational Autoencoders, and the latest applications of Generative AI in fields like healthcare and art. With 10 multiple-choice questions, some of which may have more than one correct answer, this quiz is designed to challenge your growing knowledge of Generative AI.

1 / 10

What are Generative Adversarial Networks (GANs) composed of?

Two competing neural networks

A forward and backward pass model

A single neural network

A generator and a discriminator

2 / 10

Which of the following is NOT an application of Generative AI?

Deepfake generation

Text-to-speech synthesis

Image-to-image translation

Fraud detection

3 / 10

As of now, in which of the following domains has Generative AI shown significant advancements?

Healthcare

Agriculture

Travel

Art and design

4 / 10

Which of the following models is primarily used for generating text in Generative AI?

RNN (Recurrent Neural Network)

BERT (Bidirectional Encoder Representations from Transformers)

GPT (Generative Pretrained Transformer)

CNN (Convolutional Neural Network)

5 / 10

What is the primary purpose of the generator in a GAN?

To create new, synthetic data

To supervise the discriminator

To classify data as real or fake

To generate real data

6 / 10

What is a key difference between Generative AI and Discriminative AI?

Discriminative AI creates new data

Discriminative AI classifies data

Generative AI creates new data

Generative AI classifies data

7 / 10

What is the role of the discriminator in a GAN?

To classify the generated data as real or fake

To generate new data samples

To train other neural networks

To help the generator create data

8 / 10

Which of the following are common uses of Generative AI?

Image generation

Speech synthesis

Sorting algorithms

Text generation

9 / 10

Which of the following is a key challenge in training GANs?

Discriminator overtraining

Generator underfitting

Data overfitting

Mode collapse

10 / 10

What technique does a Variational Autoencoder (VAE) use to generate new data?

By creating fake data from random noise

By using convolutional layers

By learning a probabilistic representation of the data

By using a fixed latent space

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Gen AI Basics Quiz 2

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