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Transfer Learning(DL) QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

What is the primary advantage of using a pre-trained model for transfer learning?

It guarantees better performance than training from scratch.

It allows you to skip the data preprocessing step.

It reduces the need for hyperparameter tuning.

It saves computational resources.

Question: 2

Which type of transfer learning is often used when the source and target tasks are very similar, but there is a lack of labeled data for the target task?

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Zero-shot transfer learning

Question: 3

What is the primary goal of model initialization transfer learning?

To transfer knowledge from one model to another

To train a new model from scratch

To adapt a model to a different domain

To fine-tune all layers of a pre-trained model

Question: 4

Which type of transfer learning is often used when the source and target tasks are related but not identical?

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Zero-shot transfer learning

Question: 5

What is the primary challenge in zero-shot transfer learning?

Adapting the model to the source domain

Handling the domain shift between source and target domains

Finding a suitable pre-trained model

Choosing the right learning rate

Question: 6

Which type of transfer learning is often used when there is a lack of labeled data for the target task?

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Zero-shot transfer learning

Question: 7

What is the primary advantage of using a pre-trained model as a feature extractor in feature extraction transfer learning?

It speeds up the training process.

It allows for fine-tuning of all layers.

It guarantees better performance.

It simplifies the model architecture.

Question: 8

Which type of transfer learning is most suitable for situations where computational resources are limited?

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Model initialization transfer learning

Question: 9

What is the primary advantage of feature extraction transfer learning?

It fine-tunes all layers of the pre-trained model.

It requires no changes to the pre-trained model.

It transfers all layers of the pre-trained model.

It guarantees better performance than fine-tuning.

Question: 10

In domain adaptation transfer learning, what is the target domain?

The domain where the model is first trained

The domain with the largest dataset

The domain where the model is deployed

The domain to which the model is adapted

Question: 11

What is the primary advantage of fine-tuning transfer learning?

It requires no additional training.

It adapts the model to a new task with minimal changes.

It transfers only the input layer.

It guarantees superior performance.

Question: 12

Which type of transfer learning is often used when the source and target tasks are completely unrelated?

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Zero-shot transfer learning

Question: 13

What is the primary goal of domain adaptation in transfer learning?

To transfer all layers of a pre-trained model

To adapt a model to a completely different task

To handle the differences between source and target domains

To improve the performance on the source domain

Question: 14

Which type of transfer learning is often used in natural language processing (NLP) for tasks like text classification?

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Model initialization transfer learning

Question: 15

What is the main limitation of zero-shot transfer learning?

It requires a large amount of labeled data for the target task.

It can only be applied to image-related tasks.

It may not perform as well as other transfer learning methods.

It is computationally expensive.

Question: 16

What is the primary goal of transfer learning in deep learning?

To transfer knowledge from one domain to another

To train models from scratch on each new task

To reduce the size of the neural network

To improve the learning rate

Question: 17

Which type of transfer learning is often used in computer vision for tasks like image classification?

Fine-tuning transfer learning

Feature extraction transfer learning

Domain adaptation transfer learning

Model initialization transfer learning

Question: 18

In transfer learning, what is the source domain?

The domain where the model is first trained

The domain where the model is deployed

The domain with the smallest dataset

The domain with the most diverse data

Question: 19

What is the main challenge in domain adaptation transfer learning?

Transferring all layers of the pre-trained model

Adapting to a completely different task

Addressing the domain shift between source and target domains

Ensuring the model performs perfectly on the source domain

Question: 20

Which transfer learning approach is best suited for situations where the source and target tasks are very similar?

Feature extraction transfer learning

Fine-tuning transfer learning

Domain adaptation transfer learning

Model initialization transfer learning

Question: 21

What is zero-shot transfer learning?

A transfer learning method that requires zero labeled data for the target task

A method that transfers all layers of a pre-trained model to a new task

A method that trains a new model from scratch for a target task

A method that uses a fixed learning rate for pre-trained layers

Question: 22

Which type of transfer learning is often used when the source and target domains have some similarities but also differences?

Feature extraction transfer learning

Fine-tuning transfer learning

Domain adaptation transfer learning

Zero-shot transfer learning

Question: 23

In fine-tuning transfer learning, what is the learning rate typically set for the pre-trained layers?

A high learning rate

A low learning rate

The same learning rate as the new layers

No learning rate is applied to the pre-trained layers

Question: 24

Which technique is commonly used for feature extraction transfer learning?

Fine-tuning all layers of the pre-trained model

Freezing all layers of the pre-trained model except the final layer

Training a new model from scratch

Adding more layers to the pre-trained model

Question: 25

In transfer learning, what is domain adaptation?

Adapting a model to a different domain by fine-tuning all layers

Adapting a model to a different task by transferring all layers

Adapting a model to a different domain by transferring only some layers

Adapting a model to a different task by transferring only the input layer

Question: 26

What is the primary difference between feature extraction transfer learning and fine-tuning transfer learning?

Feature extraction transfers all layers, while fine-tuning transfers only the final layer.

Feature extraction fine-tunes all layers, while fine-tuning transfers only some layers.

Feature extraction transfers all layers, while fine-tuning transfers all layers as well.

Feature extraction is not a form of transfer learning.

Question: 27

Which type of transfer learning involves fine-tuning all layers of a pre-trained model for a new task?

Feature extraction transfer learning

Fine-tuning transfer learning

Domain adaptation transfer learning

Model initialization transfer learning

Question: 28

What is the main advantage of using a pre-trained model for a new task?

It reduces the need for labeled data for the new task.

It guarantees better performance than training from scratch.

It increases the complexity of the model.

It speeds up the training process.

Question: 29

Which layer(s) of a pre-trained neural network are typically modified or fine-tuned for a new task in transfer learning?

All layers

Only the output layer

Only the input layer

Some intermediate layers

Question: 30

In transfer learning, what is a pre-trained model?

A model that has not been trained on any data

A model that has been trained on a specific task and dataset

A model that is randomly initialized

A model that is fine-tuned on a new task

Topic List

Recommended Projects

Transfer Learning(DL) QUIZ (MCQ QUESTIONS AND ANSWERS)

TIME:20:00

Time:20:00

What is the primary advantage of using a pre-trained model for transfer learning?

Which type of transfer learning is often used when the source and target tasks are very similar, but there is a lack of labeled data for the target task?

What is the primary goal of model initialization transfer learning?

Which type of transfer learning is often used when the source and target tasks are related but not identical?

What is the primary challenge in zero-shot transfer learning?

Which type of transfer learning is often used when there is a lack of labeled data for the target task?

What is the primary advantage of using a pre-trained model as a feature extractor in feature extraction transfer learning?

Which type of transfer learning is most suitable for situations where computational resources are limited?

What is the primary advantage of feature extraction transfer learning?

In domain adaptation transfer learning, what is the target domain?

What is the primary advantage of fine-tuning transfer learning?

Which type of transfer learning is often used when the source and target tasks are completely unrelated?

What is the primary goal of domain adaptation in transfer learning?

Which type of transfer learning is often used in natural language processing (NLP) for tasks like text classification?

What is the main limitation of zero-shot transfer learning?

What is the primary goal of transfer learning in deep learning?

Which type of transfer learning is often used in computer vision for tasks like image classification?

In transfer learning, what is the source domain?

What is the main challenge in domain adaptation transfer learning?

Which transfer learning approach is best suited for situations where the source and target tasks are very similar?

What is zero-shot transfer learning?

Which type of transfer learning is often used when the source and target domains have some similarities but also differences?

In fine-tuning transfer learning, what is the learning rate typically set for the pre-trained layers?

Which technique is commonly used for feature extraction transfer learning?

In transfer learning, what is domain adaptation?

What is the primary difference between feature extraction transfer learning and fine-tuning transfer learning?

Which type of transfer learning involves fine-tuning all layers of a pre-trained model for a new task?

What is the main advantage of using a pre-trained model for a new task?

Which layer(s) of a pre-trained neural network are typically modified or fine-tuned for a new task in transfer learning?

In transfer learning, what is a pre-trained model?