Deep Learning Architectures MCQs With Answers

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Deep Learning Architectures Online Quiz

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

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Deep Learning Architectures Quiz

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1 / 40

A Gated Recurrent Unit (GRU) combines ________ and ________ gates.

Input, Output

Reset, Update

Forget, Update

2 / 40

Residual connections in ResNet help mitigate ________ during training.

Underfitting

Vanishing gradients

Overfitting

3 / 40

The Long Short-Term Memory (LSTM) architecture is an improvement over ________.

GANs

RNNs

CNNs

4 / 40

The ________ architecture is designed for handling variable-length inputs and outputs in sequence-to-sequence tasks.

Transformer

LSTM

VGG

5 / 40

Recurrent Neural Networks (RNNs) are suitable for ________.

Static images

Tabular data

Sequential data

6 / 40

________ architectures have been successful in natural language processing tasks.

VGG

Transformer

ResNet

7 / 40

________ architectures are capable of generating new data samples.

RNNs

CNNs

GANs

8 / 40

________ networks use generative and discriminative components.

GANs

CNNs

RNNs

9 / 40

________ networks are used for learning from structured data.

RNNs

MLPs

CNNs

10 / 40

________ networks are useful for anomaly detection in time series data.

CNNs

Autoencoders

GANs

11 / 40

The ________ architecture is effective for capturing dependencies in sequential data.

Transformer

Autoencoder

Bi-directional LSTM

12 / 40

________ networks are suitable for image classification tasks.

CNNs

GANs

RNNs

13 / 40

GANs consist of ________ and ________ networks.

Actor, Critic

Generator, Discriminator

Encoder, Decoder

14 / 40

DenseNet improves gradient flow and feature reuse by using ________.

Recurrent connections

Skip connections

Dense connections

15 / 40

The DenseNet architecture is known for its ________ connections between layers.

Dense

Sparse

Recurrent

16 / 40

________ networks are used for reducing the number of parameters in a model.

CNNs

GANs

Pruning

17 / 40

Variational Autoencoders (VAEs) are used for ________.

Discriminative modeling

Generative modeling

Feature selection

18 / 40

The ________ architecture is widely used for text generation tasks.

GAN

Transformer

LSTM

19 / 40

In an Autoencoder, the bottleneck layer is used for ________.

Output prediction

Latent representation

Classification

20 / 40

________ are effective for learning embeddings from text data.

Word2Vec

Autoencoders

CNNs

21 / 40

The ________ architecture is effective for semantic segmentation tasks.

VGG

MobileNet

U-Net

22 / 40

________ networks are known for their ability to handle sequential data.

GANs

RNNs

CNNs

23 / 40

The U-Net architecture is commonly used in ________.

Medical image segmentation

Sentiment analysis

Speech recognition

24 / 40

Autoencoders are used for ________.

Classification

Dimensionality reduction

Sequence prediction

25 / 40

________ networks use attention mechanisms to focus on relevant parts of input sequences.

LSTM

Transformer

ResNet

26 / 40

The VGG architecture is known for its ________ layers.

Recurrent

Shallow

Deep

27 / 40

The WaveNet architecture is designed for ________ synthesis.

Speech

Video

Image

28 / 40

The ________ architecture is efficient for object detection tasks.

YOLO

ResNet

LSTM

29 / 40

The Transformer architecture is known for its application in ________.

Image classification

Natural language processing

Reinforcement learning

30 / 40

The ________ architecture is characterized by its hierarchical feature extraction.

Shallow

Deep

Recurrent

31 / 40

________ architectures excel at generating high-quality images.

RNNs

GANs

CNNs

32 / 40

________ are commonly used for time series forecasting.

CNNs

GANs

RNNs

33 / 40

In an LSTM cell, the ________ gate regulates information flow.

Output

Forget

Input

34 / 40

The Inception architecture uses ________ modules to process different scales of information.

Inception

Residual

Dense

35 / 40

________ architectures are well-suited for image super-resolution tasks.

RNNs

CNNs

GANs

36 / 40

The ResNet architecture introduces ________ connections to address vanishing gradients.

Pooling

Recurrent

Skip

37 / 40

________ are designed for handling variable-length sequences.

RNNs

CNNs

GANs

38 / 40

Convolutional Neural Networks (CNNs) are primarily used for ________.

Natural language processing

Regression

Image recognition

39 / 40

MobileNet architectures are designed for ________ environments.

Mobile

Cloud

Desktop

40 / 40

Capsule Networks aim to overcome limitations in ________.

RNNs

Autoencoders

CNNs

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