Generative Chatbots QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

Generative chatbots can adapt to:

Different languages

Different conversational styles

Different user personas

All of the above

Question: 2

What is a key advantage of generative chatbots over retrieval-based chatbots?

Higher scalability

Greater control over response quality

Lower computational requirements

Faster response times

Question: 3

Which technique is used to handle out-of-vocabulary words in generative chatbots?

OOV token replacement

Synonym substitution

Spell correction

Data augmentation

Question: 4

Generative chatbots are particularly useful in applications requiring:

Quick access to factual information

Long, open-ended conversations

Transactional interactions

Image and video processing

Question: 5

What type of generative model is typically used in dialogue generation?

Transformer

Autoencoder

LSTM

GPT (Generative Pre-trained Transformer)

Question: 6

What method is commonly used to generate responses in generative chatbots?

Markov chains

Decision trees

Hidden Markov Models (HMMs)

Neural networks

Question: 7

Generative chatbots can be vulnerable to attacks such as:

Adversarial examples

Data poisoning

Model inversion

All of the above

Question: 8

What is NOT a limitation of generative chatbots?

Limited understanding of context

Difficulty in handling rare scenarios

Dependency on large amounts of training data

Inability to generate coherent responses

Question: 9

What distinguishes generative chatbots from retrieval-based chatbots?

Generative chatbots use pre-defined responses

Generative chatbots can produce novel responses

Generative chatbots rely solely on user input

Generative chatbots cannot handle multiple turns in a conversation

Question: 10

Generative chatbots can be categorized into:

Rule-based and generative

Supervised and unsupervised

Open-domain and closed-domain

Text-based and voice-based

Question: 11

What role does reinforcement learning play in the development of generative chatbots?

Pre-training the model

Fine-tuning the model based on user feedback

Generating synthetic training data

Not used in generative chatbots

Question: 12

Which component of a generative chatbot model is responsible for understanding user intent?

Encoder

Decoder

Attention mechanism

Feedback loop

Question: 13

What technique is used to fine-tune generative chatbots for specific tasks or domains?

Transfer learning

Reinforcement learning

Unsupervised learning

Active learning

Question: 14

Which is an example of a popular generative chatbot application?

Google Maps

Facebook Messenger

Twitter

Question: 15

What is NOT an ethical concern with generative chatbots?

Privacy violations

Bias in responses

Incoherent responses

Spreading misinformation

Question: 16

What is the primary objective of generative chatbots?

To understand user queries

To generate human-like responses

To analyze data patterns

To optimize computational resources

Question: 17

Which memory mechanism is commonly used in generative chatbots to handle long-range dependencies?

Short-Term Memory (STM)

External Memory (EM)

Working Memory (WM)

Semantic Memory (SM)

Question: 18

In a generative chatbot model, what does the encoder part of the model do?

Generates responses

Understands user input

Encodes context

Trains the model

Question: 19

Which technique enhances the diversity of responses in generative chatbots?

Beam search

Greedy search

Sampling

Top-k sampling

Question: 20

What aspect is crucial for maintaining user engagement in generative chatbots?

Speed of response

Accuracy of response

Diversity of response

Length of response

Question: 21

Which of the following is NOT a potential issue with generative chatbots?

Privacy concerns

Bias in responses

Inability to generate human-like responses

Overfitting to training data

Question: 22

How is the performance of generative chatbots typically evaluated?

Precision and Recall

F1 Score

Perplexity

All of the above

Question: 23

Which sector extensively uses generative chatbots for customer interactions?

Retail

Healthcare

Transportation

Agriculture

Question: 24

How do generative chatbots handle context in conversations?

By ignoring context

By considering only immediate context

By maintaining and utilizing context throughout the conversation

By relying solely on predefined responses

Question: 25

What type of neural network is commonly used in generative chatbots?

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Deep Belief Networks (DBNs)

Radial Basis Function Networks (RBFNs)

Question: 26

Which data types can generative chatbots handle?

Text data only

Audio data only

Both text and audio data

Video data only

Question: 27

What training approach is typically used for generative chatbots?

Supervised Learning

Reinforcement Learning

Unsupervised Learning

Semi-supervised Learning

Question: 28

Which framework is commonly used for building generative chatbots?

TensorFlow

PyTorch

Django

Ruby on Rails

Question: 29

How do generative chatbots generate responses?

By selecting from predefined templates

By retrieving answers from a fixed database

By generating text based on learned patterns

By outsourcing to human operators

Question: 30

Which technology enables generative chatbots to generate human-like responses?

Natural Language Processing (NLP)

Machine Learning (ML)

Graph Theory

Optical Character Recognition (OCR)

Generative Chatbots QUIZ (MCQ QUESTIONS AND ANSWERS)

TIME:20:00

Time:20:00

Generative chatbots can adapt to:

What is a key advantage of generative chatbots over retrieval-based chatbots?

Which technique is used to handle out-of-vocabulary words in generative chatbots?

Generative chatbots are particularly useful in applications requiring:

What type of generative model is typically used in dialogue generation?

What method is commonly used to generate responses in generative chatbots?

Generative chatbots can be vulnerable to attacks such as:

What is NOT a limitation of generative chatbots?

What distinguishes generative chatbots from retrieval-based chatbots?

Generative chatbots can be categorized into:

What role does reinforcement learning play in the development of generative chatbots?

Which component of a generative chatbot model is responsible for understanding user intent?

What technique is used to fine-tune generative chatbots for specific tasks or domains?

Which is an example of a popular generative chatbot application?

What is NOT an ethical concern with generative chatbots?

What is the primary objective of generative chatbots?

Which memory mechanism is commonly used in generative chatbots to handle long-range dependencies?

In a generative chatbot model, what does the encoder part of the model do?

Which technique enhances the diversity of responses in generative chatbots?

What aspect is crucial for maintaining user engagement in generative chatbots?

Which of the following is NOT a potential issue with generative chatbots?

How is the performance of generative chatbots typically evaluated?

Which sector extensively uses generative chatbots for customer interactions?

How do generative chatbots handle context in conversations?

What type of neural network is commonly used in generative chatbots?

Which data types can generative chatbots handle?

What training approach is typically used for generative chatbots?

Which framework is commonly used for building generative chatbots?

How do generative chatbots generate responses?

Which technology enables generative chatbots to generate human-like responses?