BSCS Bachelor of Science in Computer Science: Artificial Intelligence Fundamentals 4

Question 1

What distinguishes a generative AI model from a discriminative model?

Accepted Answer

Generative models learn P(X|Y) or P(X) to synthesize data; discriminative models learn P(Y|X) for classification

Answer

Generative models learn data distributions to generate new samples, while discriminative models learn boundaries to classify existing inputs.

Question 2

In computer vision, what is the purpose of a convolutional layer in a CNN?

Accepted Answer

To apply learned filters across the input to detect local spatial patterns

Answer

Convolutional layers slide learned filters over the input, computing dot products to detect features like edges, textures, or shapes.

Question 3

What is the Frame Problem in AI?

Accepted Answer

The challenge of specifying what does NOT change when an action is performed in a world model

Answer

The Frame Problem is the challenge of efficiently representing and reasoning about which aspects of the world remain unchanged after an action.

Question 4

Which algorithm is used in Monte Carlo Tree Search (MCTS) to balance exploration and exploitation of the game tree?

Accepted Answer

Upper Confidence Bound applied to Trees (UCT)

Answer

UCT selects nodes using UCB1 formula balancing exploitation (high-value nodes) and exploration (less-visited nodes).

Question 5

In transfer learning, what is 'fine-tuning'?

Accepted Answer

Adjusting pre-trained model weights on a new task-specific dataset with a low learning rate

Answer

Fine-tuning continues training a pre-trained model on new data, typically with a small learning rate to preserve learned representations.

BSCS - Bachelor of Science in Computer Science Practice Test