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Practice Questions: Introduction to Generative AI | Artificial Intelligence (AI) for Class 9 PDF Download

Q1. What is the primary goal of Generative AI?
(a) To classify and label data
(b) To analyze existing data
(c) To generate new content similar to human-made data
(d) To create databases

Ans: (c)
Generative AI creates new data resembling human-generated content like text, images, and music.

Q2. Which of the following is NOT an application of Generative AI?
(a) Text generation
(b) Image classification
(c) Music composition
(d) Video synthesis

Ans: (b)
Image classification is typically a task for conventional AI, not generative AI, which focuses on creating new content.

Q3. In which type of learning does the model work with unlabeled data to find patterns on its own?
(a) Supervised Learning
(b) Reinforcement Learning
(c) Generative Learning
(d) Unsupervised Learning

Ans: (d)
Unsupervised learning involves the model discovering patterns without human-provided labels.

Q4. Which tool allows users to generate images from text prompts?
(a) ChatGPT
(b) GAN Paint
(c) Artbreeder
(d) Google Gemini

Ans: (c)
Artbreeder enables users to generate and modify images using text and sliders.

Q5. What is a major ethical risk associated with Generative AI?
(a) Reduced storage space
(b) Increased computing speed
(c) Generation of fake content like deepfakes
(d) Lack of mobile support

Ans: (c)
One key ethical issue is the creation of fake media, which can spread misinformation and harm public trust.

Q6. Which of the following is a text-based Generative AI tool?
(a) Runway ML
(b) Artbreeder
(c) ChatGPT
(d) GAN Paint

Ans: (c)
ChatGPT generates human-like text and is a popular example of a text-based generative AI tool.

​Q7. How is Generative AI different from Conventional AI in terms of output?
(a) Generative AI output is always factual.
(b) Generative AI provides predictable results.
(c) Generative AI produces creative and novel content.
(d) There is no difference.

Ans: (c)
Generative AI creates unexpected, innovative content, while conventional AI outputs are based on predefined rules or patterns.

Q8. Which of the following is a correct match?
(a) AIVA – Image generation
(b) Runway ML – GAN and VAE-based modeling
(c) GAN Paint – Text summarization
(d) ChatGPT – Image enhancement

Ans: (b)
Runway ML allows users to create generative models like GANs and VAEs for image and video generation.

Q9. What is a responsible practice when using Generative AI?
(a) Ignoring the dataset origin
(b) Publishing all generated outputs without review
(c) Ensuring diverse and representative training data
(d) Using only one dataset for all outputs

Ans: (c)
Responsible use includes using diverse datasets to avoid bias and ensure fairness in generated content.

Q10. Which of the following fields can benefit from Generative AI?
(a) Music
(b) Architecture
(c) Fashion design
(d) All of the above

Ans: (d)
Generative AI has applications across many creative fields like music, architecture, and fashion.

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FAQs on Practice Questions: Introduction to Generative AI - Artificial Intelligence (AI) for Class 9

1. What is Generative AI and how does it differ from traditional AI?
Ans.Generative AI refers to a subset of artificial intelligence systems that can create new content, such as text, images, music, or even video, based on the input data they have been trained on. Unlike traditional AI, which typically focuses on recognizing patterns and making decisions based on existing data, Generative AI actively generates new outputs, enabling it to produce creative works. For example, while a traditional AI might analyze and categorize images, a Generative AI can create entirely new images that resemble the style of those in its training set.
2. What are some common applications of Generative AI?
Ans.Common applications of Generative AI include content creation for writing articles, generating artwork, designing video game characters, composing music, and even developing synthetic voices for virtual assistants. Additionally, it is used in fields like fashion design, marketing for personalized advertisements, and in research for drug discovery by simulating molecular structures.
3. What ethical considerations arise from the use of Generative AI?
Ans.The use of Generative AI raises several ethical considerations, including issues of copyright and ownership of the generated content, the potential for misuse in creating deepfakes, and the need for transparency in how these systems operate. Ensuring that Generative AI does not perpetuate biases present in the training data is also a significant concern, as it can lead to harmful stereotypes or misinformation being disseminated.
4. How does Generative AI learn from data?
Ans.Generative AI learns from data through a process called training, where it is exposed to large datasets that contain examples of the type of content it is expected to generate. During this process, it identifies patterns and relationships within the data, adjusting its internal parameters to improve its output. This is often done using techniques like neural networks, which simulate the way human brains process information, allowing the AI to create new content that mimics the style and structure of its training data.
5. What are some challenges associated with Generative AI?
Ans.Some challenges associated with Generative AI include the need for large amounts of high-quality training data, which can be difficult and time-consuming to gather. Additionally, ensuring the quality and relevance of the generated content can be tricky, as the AI might produce outputs that are nonsensical or irrelevant to the intended purpose. There are also technical challenges related to the computational resources required for training and running Generative AI models, which can be significant.
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