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Generative AI

Complete Subject Question Bank

Subject Overview
Explore the fundamentals of Artificial Intelligence, neural networks, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer architectures.
Part 1 — Introduction to Generative AI#1

Generative AI primarily aims to:

A
Classify inputs
B
Predict stock prices only
C
Create new data samples similar to training data
D
Only compress data

Generative AI creates new data samples (text, images, audio, etc.) by learning the underlying statistical distribution of training data, rather than just classifying or predicting existing patterns.

Part 1 — Introduction to Generative AI#2

Which of these is NOT typically produced by generative models?

A
Images
B
Labels for classification tasks
C
Music
D
Text

The correct answer is: Labels for classification tasks.

Part 1 — Introduction to Generative AI#3

Learning a data distribution p(x) allows a model to:

A
Compute p(y|x)
B
Sample new plausible data points
C
Only memorize training data
D
Always achieve perfect reconstruction

The correct answer is: Sample new plausible data points.

Part 1 — Introduction to Generative AI#4

Which statement best contrasts discriminative and generative models?

A
Discriminative models learn p(x), generative learn p(y|x)
B
Discriminative models learn p(y|x), generative learn p(x) or p(x,y)
C
They are identical
D
Generative models cannot be used for classification

Discriminative models learn p(y|x) — the conditional probability of a label given input. Generative models learn p(x) or the joint p(x,y), enabling them to sample new data.

Part 1 — Introduction to Generative AI#5

Which is a common application of generative AI?

A
Data augmentation
B
Direct OS kernel development
C
Manufacturing hardware
D
Network routing protocols

The correct answer is: Data augmentation.

Part 1 — Introduction to Generative AI#6

Generative AI that helps artists by suggesting concepts is an example of:

A
Autonomous replacement
B
Creative tool / collaborator
C
Discriminative learning
D
Feature extraction only

The correct answer is: Creative tool / collaborator.

Part 1 — Introduction to Generative AI#7

A model that learns to produce plausible human faces has learned approximations of:

A
p(y|x)
B
p(x)
C
loss landscapes only
D
an SVM decision boundary

The correct answer is: p(x).

Part 1 — Introduction to Generative AI#8

Which capability is NOT typical of generative models?

A
Simulation for training
B
Creating synthetic data
C
Guaranteed unbiased outputs
D
Art assistance

The correct answer is: Guaranteed unbiased outputs.

Part 1 — Introduction to Generative AI#9

Which of the following is a risk specifically mentioned for generative AI?

A
Deepfakes and misinformation
B
Faster compilers
C
Lower memory usage
D
Stable training always

The correct answer is: Deepfakes and misinformation.

Part 1 — Introduction to Generative AI#10

Text generation, image generation and music generation are examples of:

A
Discriminative tasks
B
Supervised regression
C
Generative tasks
D
Clustering tasks

The correct answer is: Generative tasks.

Part 1 — Introduction to Generative AI#11

Why is learning a distribution more powerful than memorizing examples?

A
It guarantees exact copies
B
It allows sampling novel but plausible items
C
It reduces compute to zero
D
It avoids any bias automatically

Learning a data distribution allows a model to generate novel but plausible samples, unlike mere memorization which can only reproduce training examples.

Part 1 — Introduction to Generative AI#12

Which of these is a direct benefit of synthetic data?

A
Reduces need for any validation
B
Helps train models where real data is scarce
C
Removes need for GPUs
D
Ensures perfect model fairness

Synthetic data generated by generative models supplements real datasets, especially where real data is scarce, sensitive, or expensive to collect.

Part 1 — Introduction to Generative AI#13

A generative model that outputs new molecules would be used in:

A
Drug discovery
B
Network security
C
Compiler optimizations
D
Operating system design

The correct answer is: Drug discovery.

Part 1 — Introduction to Generative AI#14

Which term best describes creating content that resembles training data but is not identical?

A
Overfitting
B
Generalization / generation
C
Discrimination
D
Regularization

The correct answer is: Generalization / generation.

Part 1 — Introduction to Generative AI#15

Generative AI differs from classification because it focuses on:

A
Label boundaries
B
Creating samples
C
Only supervised labels
D
Feature scaling

The correct answer is: Creating samples.

Part 2 — History & Foundations#16

Gaussian Mixture Models (GMMs) are examples of:

A
Implicit models
B
Classical probabilistic models
C
Transformer-based models
D
Adversarial networks

The correct answer is: Classical probabilistic models.

Part 2 — History & Foundations#17

Hidden Markov Models (HMMs) are especially useful for:

A
Image synthesis
B
Sequence modeling like speech
C
Style transfer for images
D
Transformer pretraining

The correct answer is: Sequence modeling like speech.

Part 2 — History & Foundations#18

Which breakthrough enabled deep generative models to scale in the 2010s?

A
Larger datasets and GPUs
B
Smaller datasets
C
Removal of backpropagation
D
Replacing neural nets with SVMs

The correct answer is: Larger datasets and GPUs.

Part 2 — History & Foundations#19

The VAE paper was published by:

A
Goodfellow et al.
B
Kingma & Welling
C
Vaswani et al.
D
Hinton alone

The correct answer is: Kingma & Welling.

Part 2 — History & Foundations#20

GANs introduced the idea of:

A
Autoencoding
B
A generator vs a discriminator adversarial training
C
Self-attention
D
Reinforcement learning

GANs (Generative Adversarial Networks), introduced by Goodfellow et al. (2014), set up an adversarial game between a generator (creates fake samples) and a discriminator (distinguishes real from fake).

Key Topics to Study

Based on our question bank analysis, master these concepts to score high in Generative AI.

GenerativeGANsVAEsTransformersAttentionRNNLSTMTraining
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