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Foundation Models Quiz

Total questions: 10

Worksheet time: 5mins

Name
Class
Date
1.

What is a foundation model?

a)

A model trained for a single, narrow task

b)

A small model used for initialization

c)

A large pre-trained model adaptable to multiple tasks

d)

A model specifically for reinforcement learning

2.

Which of the following is a foundation model?

a)

SVM

b)

ResNet-18

c)

GPT-4

d)

k-NN

3.

Foundation models are usually trained using:

a)

Supervised learning only

b)

Unsupervised or self-supervised learning on massive data

c)

Reinforcement learning

d)

Small labeled datasets

4.

What enables foundation models to generalize across tasks?

a)

Task-specific optimization

b)

Handcrafted features

c)

Large-scale training on diverse data

d)

Shallow architecture

5.

Which of the following is a challenge in foundation models?

a)

Lack of scalability

b)

Inability to pre-train

c)

Hallucination and bias

d)

Too few parameters

6.

What is fine-tuning in the context of foundation models?

a)

Reducing model parameters

b)

Training from scratch

c)

Adapting a pre-trained model to a specific task

d)

Testing the model performance

7.

Which of the following foundation models is designed for both vision and language tasks?

a)

YOLO

b)

BERT

c)

CLIP

d)

DeepSpeech

8.

What is a key capability of foundation models that makes them effective across multiple tasks?

a)

Fixed architecture and parameters

b)

Extensive use of labeled datasets only

c)

Pre-training on broad data followed by task-specific adaptation

d)

Limitation to supervised learning methods

9.

Which of the following is a key benefit of using foundation models?

a)

Require labeled data only

b)

Cannot generalize

c)

Reusability across domains

d)

Low accuracy on NLP tasks

10.

A foundation model like GPT is primarily based on which architecture?

a)

CNN

b)

RNN

c)

Decision Trees

d)

Transformer