Students find these Class 9 AI MCQ Chapter 6 Introduction to Generative AI Class 9 MCQ Online Test with Answers helpful for self-assessment and preparation.
Class 9 Introduction to Generative AI MCQ
MCQ on Introduction to Generative AI Class 9
Class 9 Introduction to Generative AI MCQ – Introduction to Generative AI MCQ Class 9
Multiple Choice Questions
Question 1.
What is Generative Al primarily focused on?
(a) Completing tasks
(b) Analyzing data
(c) Creating new content
(d) Understanding human instructions
Answer:
(c) Creating new content
Question 2.
When was the concept of machines capable of reasoning similarly to humans introduced?
(a) 1956
(b) 1958
(c) 1964
(d) 1950
Answer:
(d) 1950
Question 3.
What was the first functioning generative AI created in 1964?
(a) GAN
(b) LSTM
(c) ELIZA chatbot
(d) Transformer
Answer:
(c) ELIZA chatbot
Question 4.
Which milestone in 2014 was a breakthrough in generative AI for generating high-quality images?
(a) RNN creation
(b) LSTM development
(c) GAN creation
(d) VAE creation
Answer:
(c) GAN creation
Question 5.
Which type of Al is most commonly used for tasks such as image and speech synthesis?
(a) Conventional AI
(b) Generative AI
(c) Symbolic AI
(d) Expert systems
Answer:
(b) Generative AI
Question 6.
Which model is specifically designed to handle sequential data like text or speech?
(a) VAE
(b) GAN
(c) RNN
(d) Transformer
Answer:
(c) RNN
Question 7.
What is the primary application of GANs?
(a) Drug discovery
(b) Music composition
(c) Fashion design
(d) All of these
Answer:
(d) All of these
Question 8.
Which advantage of Generative Al is highlighted by its use in personalized marketing content?
(a) Creativity and Content Generation
(b) Efficiency and Automation
(c) Enhanced Personalization
(d) Innovation in Science and Medicine
Answer:
(c) Enhanced Personalization
Question 9.
Which disadvantage of Generative AI is exemplified by the potential for biased outcomes in facial recognition?
(a) Data Bias
(b) Uncertainty
(c) Computational Demands
(d) Ethical Concerns
Answer:
(a) Data Bias
Question 10.
What is one application of Generative AI mentioned in the context of gaming?
(a) Language Translation
(b) Drug Discovery
(c) Simulation and Modeling
(d) Creating dynamic game environments
Answer:
(d) Creating dynamic game environments
Question 11.
Which Generative AI tool is specifically designed for code generation and completion?
(a) DALL-E
(b) GPT
(c) MuseNet
(d) StoryAI
Answer:
(b) GPT
Question 12.
What ethical concern arises from the potential use of Generative AI to create fake content?
(a) Data Bias
(b) Job displacement
(c) Misinformation
(d) Privacy risks
Answer:
(c) Misinformation
Question 13.
Which solution is suggested to mitigate bias in AI outputs?
(a) Scrutiny
(b) Training data diversity
(c) User privacy
(d) Ownership guidelines
Answer:
(b) Training data diversity
Question 14.
In which year was GPT-4 released?
(a) 2019
(b) 2020
(c) 2021
(d) 2023
Answer:
(d) 2023
Question 15.
Which type of AI relies on explicit programming and predefined rules?
(a) Generative AI
(b) Conventional AI
(c) Deep learning
(d) Supervised learning
Answer:
(b) Conventional AI
Question 16.
Which model is specifically designed for image generation based on textual descriptions?
(a) GPT
(b) RNN
(c) DALL-E
(d) VAE
Answer:
(c) DALL-E
Question 17.
What advantage of Generative AI is highlighted by its application in drug discovery?
(a) Creativity and Content Generation
(b) Enhanced Personalization
(c) Innovation in Science and Medicine
(d) Efficiency and Automation
Answer:
(c) Innovation in Science and Medicine
Question 18.
Which disadvantage of Generative Al is exemplified by the need for substantial computational resources?
(a) Data Bias
(b) Uncertainty
(c) Computational Demands
(d) Ethical Concerns
Answer:
(c) Computational Demands
Statement Based Questions
Question 1.
Statement 1: Generative AI can create new content such as text, images, music, and videos.
Statement 2: Generative AI models operate using machine learning, particularly deep learning.
(a) Both Statement 1 and Statement 2 are correct
(b) Both Statement 1 and Statement 2 are incorrect
(c) Statement 1 is correct but Statement 2 is incorrect
(d) Statement 2 is correct but Statement 1 is incorrect
Answer:
(a) Both Statement 1 and Statement 2 are correct
Question 2.
Statement 1: VAEs (Variational Autoencoders) are used for generating new data by learning the underlying structure of a dataset.
Statement 2: GANs (Generative Adversarial Networks) use two neural networks, Generator and Discriminator, in an adversarial relationship to create new data.
(a) Both Statement 1 and Statement 2 are correct
(b) Both Statement 1 and Statement 2 are incorrect
(c) Statement 1 is correct but Statement 2 is incorrect
(d) Statement 2 is correct but Statement 1 is incorrect
Answer:
(a) Both Statement 1 and Statement 2 are correct
Question 3.
Statement 1: RNNs (Recurrent Neural Networks) are ideal for sequential data processing due to their ability to consider past information.
Statement 2: Transformer models rely on self-attention mechanisms to understand relationships between different parts of a sequence.
(a) Both Statement 1 and Statement 2 are correct
(b) Both Statement 1 and Statement 2 are incorrect
(c) Statement 1 is correct but Statement 2 is incorrect
(d) Statement 2 is correct but Statement 1 is incorrect
Answer:
(a) Both Statement 1 and Statement 2 are correct
Question 4.
Statement 1: Generative Al is a subset of conventional Al that relies on predefined rules for generating new content.
Statement 2: Generative Al can lead to job displacement as it automates tasks like content creation.
(a) Both Statement 1 and Statement 2 are correct
(b) Both Statement 1 and Statement 2 are incorrect
(c) Statement 1 is correct but Statement 2 is incorrect
(d) Statement 2 is correct but Statement 1 is incorrect
Answer:
(d) Statement 2 is correct but Statement 1 is incorrect
Assertion & Reason Based Questions
Question 1.
Assertion: Generative AI models can create new content, such as text, images, music, and videos, without any human instructions.
Reason: These models are trained to understand patterns and structures within existing data and use that knowledge to generate new original data.
(a) Both the assertion and reason are true, and the reason is the correct explanation of the assertion.
(b) Both the assertion and reason are true, but the reason is not the correct explanation of the assertion.
(c) The assertion is true, but the reason is false.
(d) Both the assertion and reason are false.
Answer:
(a) Both the assertion and reason are true, and the reason is the correct explanation of the assertion.
Question 2.
Assertion: Generative Adversarial Networks (GANs) consist of two main components – a Generator Network and a Discriminator Network.
Reason: The Generator network creates new data, while the Discriminator network tries to identify whether the data it sees is real or generated by the Generator.
(a) Both the assertion and reason are true, and the reason is the correct explanation of the assertion.
(b) Both the assertion and reason are true, but the reason is not the correct explanation of the assertion.
(c) The assertion is true, but the reason is false.
(d) Both the assertion and reason are false.
Answer:
(a) Both the assertion and reason are true, and the reason is the correct explanation of the assertion.
Question 3.
Assertion: Recurrent Neural Networks (RNNs) are particularly suitable for tasks like language processing, where the meaning of a word depends on the words before it.
Reason: RNNs have an internal state, often called a hidden layer, that acts like a memory and stores information about past inputs.
(a) Both the assertion and reason are true, and the reason is: the correct explanation of the assertion.
(b) Both the assertion and reason are true, but the reason is not the correct explanation of the assertion.
(c) The assertion is true, but the reason is false.
(d) Both the assertion and reason are false.
Answer:
(a) Both the assertion and reason are true, and the reason is: the correct explanation of the assertion.
Question 4.
Assertion: Transformer models have become the go-to choice for many Natural Language Processing (NLP) tasks due to their ability to analyze the relationships between different parts of a sequence.
Reason: Unlike older models, transformer models rely on self-attention mechanisms, allowing them to understand how words depend on each other across long distances in a sentence.
(a) Both the assertion and reason are true, and the reason is the correct explanation of the assertion.
(b) Both the assertion and reason are true, but the reason is not the correct explanation of the assertion.
(c) The assertion is true, but the reason is false.
(d) Both the assertion and reason are false.
Answer:
(a) Both the assertion and reason are true, and the reason is the correct explanation of the assertion.
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