Transfer Learning MCQs With Answers

Computer MCQs 360April 9, 2024

0 17 1 minute read

Welcome to the Transfer Learning MCQs with Answers. In this post, we have shared Transfer Learning Online Test for different competitive exams. Find practice Transfer Learning Practice Questions with answers in Computer Tests exams here. Each question offers a chance to enhance your knowledge regarding Transfer Learning.

Transfer Learning Online Quiz

By presenting 3 options to choose from, Transfer Learning Quiz which cover a wide range of topics and levels of difficulty, making them adaptable to various learning objectives and preferences. You will have to read all the given answers of Transfer Learning Questions and Answers and click over the correct answer.

Test Name: Transfer Learning MCQ Quiz Practice
Type: Quiz Test
Total Questions: 40
Total Marks: 40
Time: 40 minutes

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Transfer Learning

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1 / 40

What is the role of the source domain in Transfer Learning?

Regularize weights

Provide knowledge

Evaluate model performance

2 / 40

________ is a challenge in Transfer Learning where source domain data may not cover all target domain scenarios.

Gradient boosting

Regularization

Insufficient coverage

3 / 40

What is the purpose of adversarial domain adaptation in Transfer Learning?

Optimize loss function

Normalize data

Enhance domain alignment

4 / 40

________ is an approach in Transfer Learning where only a subset of layers are reused.

Partial Transfer

Complete Transfer

Domain adaptation

5 / 40

What is the primary benefit of using pretrained embeddings in Transfer Learning?

Optimize convergence

Capture semantic meanings

Normalize data

6 / 40

What is the primary benefit of using pretrained models in Transfer Learning?

Save computational resources

Optimize loss function

Normalize data

7 / 40

What does the term "target task" refer to in Transfer Learning?

Optimize loss function

Feature extraction

Task of interest

8 / 40

________ is a common application area for Transfer Learning.

Data encryption

Image classification

Database management

9 / 40

________ is a method to enhance Transfer Learning by leveraging multiple source domains.

Feature selection

Multi-source Transfer

Data augmentation

10 / 40

________ is an approach in Transfer Learning where knowledge is transferred from multiple tasks to a single target task.

Reinforcement learning

Dimensionality reduction

Multi-task learning

11 / 40

What is the primary goal of fine-tuning in Transfer Learning?

Adapt model to target domain

Normalize data

Optimize loss function

12 / 40

What does the term "pretraining" refer to in Transfer Learning?

Feature selection

Optimize loss function

Train on source data

13 / 40

What is Transfer Learning?

Data encryption

Reinforcement learning

Utilizing knowledge

14 / 40

________ is a type of Transfer Learning where the source and target tasks have different input features but the same output labels.

Domain adaptation

Feature extraction

Inductive transfer

15 / 40

What is the goal of fine-tuning in Transfer Learning?

Optimize loss function

Adapt model to new task

Train model from scratch

16 / 40

________ is a technique to reduce the dependency on labeled data in Transfer Learning.

Principal component analysis

Semi-supervised transfer

Gradient descent

17 / 40

________ is a characteristic of Transfer Learning where performance depends on domain similarity.

Domain dependency

Regularization

Feature extraction

18 / 40

________ is a challenge in Transfer Learning where the source and target domains have different distributions.

Feature extraction

Regularization

Domain shift

19 / 40

________ is an example of a pretraining task in Transfer Learning for natural language processing.

Reinforcement learning

Clustering

Language modeling

20 / 40

What is the role of instance weighting in Transfer Learning?

Normalize data

Adjust sample importance

Optimize loss function

21 / 40

________ is a strategy to select which layers to transfer in Transfer Learning.

Random initialization

Layer selection

Data augmentation

22 / 40

What is the role of adversarial training in Transfer Learning?

Optimize loss function

Align data distributions

Evaluate model performance

23 / 40

What is the goal of domain adaptation in Transfer Learning?

Optimize loss function

Align data distributions

Normalize data

24 / 40

________ is a technique to align feature distributions in Transfer Learning.

Regularization

Feature alignment

Data augmentation

25 / 40

________ is the primary advantage of Transfer Learning.

High computational cost

Specific to one task

Requires less data

26 / 40

________ is a technique to handle the semantic gap between source and target domains in Transfer Learning.

Regularization

Semantic bridging

Gradient boosting

27 / 40

________ is an example of a domain adaptation technique in Transfer Learning.

Adversarial adaptation

Support Vector Machine

Decision tree

28 / 40

What is the purpose of feature extraction in Transfer Learning?

Normalize data

Optimize loss function

Extract relevant features

29 / 40

________ is a technique to align source and target distributions in Transfer Learning.

K-means clustering

Distribution alignment

Reinforcement learning

30 / 40

________ is a method to evaluate the performance of a transferred model in Transfer Learning.

Target task evaluation

Train from scratch

Decision tree

31 / 40

What is the primary challenge in unsupervised domain adaptation?

High computational cost

Feature selection

Lack of labeled data

32 / 40

What does the term "source domain" refer to in Transfer Learning?

Validation set

Target task/data source

Original task/data source

33 / 40

________ is a method to improve model robustness in Transfer Learning against domain shifts.

Decision tree

Robust optimization

Batch normalization

34 / 40

________ is a technique used to freeze layers during Transfer Learning.

Feature extraction

Batch normalization

Fine-tuning

35 / 40

________ is a technique to measure similarity between domains in Transfer Learning.

Regularization

Feature extraction

Domain similarity

36 / 40

________ is an example of a dataset commonly used for Transfer Learning in computer vision.

Decision tree

Regression analysis

ImageNet

37 / 40

What is the primary challenge in Transfer Learning?

Linear regression

Domain mismatch

High computational cost

38 / 40

What is the role of adaptation layers in Transfer Learning?

Bridge domain gaps

Optimize loss function

Feature selection

39 / 40

________ is a method to overcome the overfitting issue in Transfer Learning.

Regularization

Dropout

Gradient boosting

40 / 40

________ is a technique to estimate domain shifts in Transfer Learning.

Gradient descent

Regularization

Domain adaptation estimator

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Computer MCQs 360April 9, 2024

0 17 1 minute read