Deep Learning MCQs With Answers

Computer MCQs 360April 29, 2024

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

Deep Learning Online Quiz

By presenting 3 options to choose from, Deep 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 Deep Learning Questions and Answers and click over the correct answer.

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

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Deep Learning

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1 / 40

________ is a technique used to prevent overfitting in Deep Learning.

Dropout

Data augmentation

Batch normalization

2 / 40

________ is a technique used to reduce the dimensionality of data.

Decision tree

Autoencoder

Support Vector Machine (SVM)

3 / 40

________ is a technique used to interpret model predictions.

Regularization

Data augmentation

Explainable AI

4 / 40

What does the term "deep" refer to in Deep Learning?

High computational cost

Multiple layers

Large dataset

5 / 40

What is the main advantage of using deep neural networks?

Handle categorical data

Optimize loss function

Learn complex patterns

6 / 40

________ is an architecture designed for processing sequential data efficiently.

Transformer

Autoencoder

Decision tree

7 / 40

________ is a technique used to handle vanishing gradient problems.

Regularization

Data augmentation

Gradient clipping

8 / 40

What does the term "backpropagation" refer to in Deep Learning?

Update model weights

Optimize loss function

Gradient descent

9 / 40

________ is an architecture used for generating human-like text.

Autoencoder

Transformer

Recurrent Neural Network (RNN)

10 / 40

________ is a type of neural network that excels at image recognition tasks.

Autoencoder

Recurrent Neural Network

Convolutional Neural Network

11 / 40

________ is an architecture designed to process sequences of data.

Recurrent Neural Network (RNN)

Support Vector Machine (SVM)

Decision tree

12 / 40

What is the purpose of dropout in neural networks?

Optimize loss function

Prevent overfitting

Normalize data

13 / 40

________ is used to evaluate the performance of a model on unseen data.

Validation set

Model training

Feature selection

14 / 40

What is the purpose of batch normalization in Deep Learning?

Optimize loss function

Improve convergence

Dimensionality reduction

15 / 40

________ is a technique to improve model generalization in Deep Learning.

Feature importance

Dimensionality reduction

Regularization

16 / 40

________ is a method to handle imbalanced datasets in Deep Learning.

SMOTE

Regularization

Principal component analysis

17 / 40

________ is a type of unsupervised learning used for feature learning.

Autoencoder

Reinforcement learning

Decision tree

18 / 40

________ is a technique used to learn representations from unlabeled data.

Support Vector Machine (SVM)

Self-Supervised Learning

Decision tree

19 / 40

What is the purpose of the Adam optimizer in Deep Learning?

Adaptive learning rate

Optimize loss function

Normalize data

20 / 40

________ is a method to initialize neural network weights effectively.

Regularization

Principal component analysis

Xavier initialization

21 / 40

What is the role of activation functions in Deep Learning?

Introduce non-linearity

Optimize loss function

Normalize data

22 / 40

What is the goal of transfer learning in Deep Learning?

Optimize loss function

Transfer knowledge

Normalize data

23 / 40

What is the goal of data augmentation in Deep Learning?

Normalize data

Increase dataset diversity

Reduce model complexity

24 / 40

________ is a technique to generate new data samples from existing data.

Generative Adversarial Network (GAN)

Naive Bayes

K-nearest neighbors

25 / 40

________ is an architecture designed for handling sequential data.

K-means clustering

Long Short-Term Memory (LSTM)

Decision tree

26 / 40

What is Deep Learning?

Neural network

Regression analysis

Decision tree

27 / 40

________ is a method to align data distributions between different domains.

Feature selection

Domain adaptation

Model training

28 / 40

________ is a type of activation function commonly used in Deep Learning.

Tanh

ReLU (Rectified Linear Unit)

Sigmoid

29 / 40

________ is an architecture used for image generation.

Generative Adversarial Network (GAN)

Autoencoder

Recurrent Neural Network

30 / 40

What is the advantage of using Convolutional Neural Networks (CNNs) for image processing tasks?

Spatial hierarchies

Handle time-series data

Optimize loss function

31 / 40

What is the purpose of pooling layers in Convolutional Neural Networks?

Downsample feature maps

Train neural networks

Regularize weights

32 / 40

________ is a technique used to train models with less labeled data.

Reinforcement learning

Semi-supervised learning

Ensemble learning

33 / 40

________ is a technique used to generate captions for images.

Dimensionality reduction

Image captioning

Clustering

34 / 40

________ is a type of neural network used for time-series forecasting.

Autoencoder

Convolutional Neural Network

Recurrent Neural Network (RNN)

35 / 40

________ is used to understand the importance of features in Deep Learning models.

Batch normalization

Feature importance

Kernel method

36 / 40

________ is used to optimize neural network weights during training.

Gradient descent

Backpropagation

K-means clustering

37 / 40

What is the purpose of the loss function in Deep Learning?

Regularize weights

Measure model performance

Train neural networks

38 / 40

________ is an unsupervised learning technique used for clustering data.

Gradient boosting

K-means clustering

Decision tree

39 / 40

________ is a technique used to reduce the complexity of neural networks.

Ensemble learning

Pruning

Regularization

40 / 40

________ is a technique used to update neural network weights layer by layer.

Regularization

Layer-wise pretraining

Data augmentation

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Computer MCQs 360April 29, 2024

0 21 1 minute read