UNIT 7: Generative AI

Why Math is the Secret Ingredient Behind AI and Data Science

May 16, 2025

Unit 3: Making Machines See – Computer Vision

July 23, 2025

108

Generative AI

MCQs

1. What is Generative AI primarily used for?
a) Classifying data
b) Generating new content similar to training data
c) Performing mathematical calculations
d) Optimizing database performance
Answer: b) Generating new content similar to training data

2. Which of the following is NOT a type of Generative AI application?
a) Image generation
b) Video generation
c) Data classification
d) Audio generation
Answer: c) Data classification

3. What distinguishes Generative AI from Discriminative AI?
a) Generative AI creates new data, while Discriminative AI classifies existing data
b) Generative AI identifies patterns in labeled data
c) Discriminative AI generates new data samples
d) Discriminative AI does not use machine learning
Answer: a) Generative AI creates new data, while Discriminative AI classifies existing data

4. Which of the following is an example of Generative AI?
a) ChatGPT
b) Decision Trees
c) Linear Regression
d) Naïve Bayes Classifier
Answer: a) ChatGPT

5. What is the role of Large Language Models (LLMs) in AI?
a) Creating and classifying text
b) Sorting numerical datasets
c) Translating programming languages
d) Running optimization algorithms
Answer: a) Creating and classifying text

6. Generative Adversarial Networks (GANs) consist of which two components?
a) Generator and Discriminator
b) Encoder and Decoder
c) Classifier and Regressor
d) Training and Testing Modules
Answer: a) Generator and Discriminator

7. Which AI model is best suited for creating realistic images?
a) GANs
b) SVM
c) Decision Trees
d) Logistic Regression
Answer: a) GANs

8. What is the primary function of a Discriminative AI model?
a) Generate new data
b) Differentiate between data categories
c) Create images
d) Write human-like text
Answer: b) Differentiate between data categories

9. What does a Variational Autoencoder (VAE) do?
a) Translates languages
b) Encodes and decodes data into latent space
c) Identifies spam emails
d) Performs clustering
Answer: b) Encodes and decodes data into latent space

10. Which company developed Gemini AI?
a) OpenAI
b) Google
c) Microsoft
d) Meta
Answer: b) Google

11. Deepfakes are created using which technology?
a) GANs
b) Reinforcement Learning
c) Decision Trees
d) Clustering
Answer: a) GANs

12. Which ethical concern is associated with Generative AI?
a) Data bias
b) Computational efficiency
c) Reduced storage capacity
d) Increased energy consumption
Answer: a) Data bias

13. What is the main drawback of LLMs?
a) Lack of computational power
b) Generation of factually incorrect information
c) Inability to process text
d) No real-world applications
Answer: b) Generation of factually incorrect information

14. Which AI technique is used in music generation?
a) Voicebox
b) Decision Trees
c) Support Vector Machines
d) K-Nearest Neighbors
Answer: a) Voicebox

15. Which tool is NOT an example of Generative AI?
a) Canva
b) ChatGPT
c) Google Sheets
d) DALL-E
Answer: c) Google Sheets

16. Which Generative AI tool specializes in video creation?
a) Lumiere
b) Perplexity AI
c) MidJourney
d) SVM
Answer: a) Lumiere

17. What do Transformers do in LLMs?
a) Process large amounts of text data efficiently
b) Convert images into text
c) Generate deepfake videos
d) Predict stock market trends
Answer: a) Process large amounts of text data efficiently

18. What is one way to prevent biases in AI-generated content?
a) Use diverse training data
b) Reduce dataset size
c) Only use labeled data
d) Avoid deep learning models
Answer: a) Use diverse training data

19. What is the main concern with AI-generated text in academic writing?
a) Transparency
b) Storage issues
c) Speed of text generation
d) Programming complexity
Answer: a) Transparency

20. Which model is optimized for text-based prompts?
a) Gemini-Pro
b) Gemini-Vision
c) GANs
d) DBMs
Answer: a) Gemini-Pro

21. Which company created LLaMA?
a) Google
b) OpenAI
c) Meta
d) Microsoft
Answer: c) Meta

22. What does “latent space” refer to in AI?
a) Compressed data representation
b) The output layer of neural networks
c) Memory storage
d) The final decision boundary
Answer: a) Compressed data representation

23. Which AI model is best for detecting anomalies in data?
a) VAEs
b) Decision Trees
c) Clustering Algorithms
d) Linear Regression
Answer: a) VAEs

24. How do chatbots use Generative AI?
a) To generate responses dynamically
b) To classify customer queries
c) To analyze user preferences
d) To perform sentiment analysis
Answer: a) To generate responses dynamically

25. Which AI model is best for summarizing text?
a) GPT-4
b) CNN
c) Random Forest
d) Decision Trees
Answer: a) GPT-4

26. What is an example of AI-generated plagiarism?
a) Using AI-generated text without citation
b) Writing an essay manually
c) Summarizing an article
d) Using a spell-checker
Answer: a) Using AI-generated text without citation

27. What type of learning is used in Generative AI?
a) Unsupervised
b) Supervised
c) Reinforcement
d) Semi-Supervised
Answer: a) Unsupervised

28. Which is a disadvantage of AI-generated content?
a) Lack of originality
b) Increased efficiency
c) Faster content creation
d) Lower resource usage
Answer: a) Lack of originality

29. What is the purpose of Gemini API?
a) Creating chatbots
b) Making images
c) Processing video files
d) Running SQL queries
Answer: a) Creating chatbots

30. Which AI model is used in face recognition?
a) CNNs
b) LSTMs
c) Decision Trees
d) GANs
Answer: a) CNNs

31. What is a major risk of using Generative AI in media?
a) Overproduction of content
b) Creation of misinformation and deepfakes
c) Increased server storage requirements
d) Slower internet speeds
Answer: b) Creation of misinformation and deepfakes

32. Which is an example of AI-generated video modification?
a) Deepfake
b) Decision Tree Algorithm
c) Logistic Regression
d) Neural Style Transfer
Answer: a) Deepfake

33. What makes Generative AI useful in education?
a) It can generate personalized learning content
b) It replaces human teachers
c) It prevents plagiarism
d) It reduces internet usage
Answer: a) It can generate personalized learning content

34. Which AI tool helps generate marketing copy?
a) ChatGPT
b) Tableau
c) Microsoft Excel
d) AutoML
Answer: a) ChatGPT

35. What is the primary ethical concern of AI-generated job applications?
a) Biased hiring decisions
b) Slow application processing
c) Increased hiring costs
d) Lack of job opportunities
Answer: a) Biased hiring decisions

36. How do LLMs process human language effectively?
a) By using deep learning and transformers
b) By translating text into binary code
c) By manually tagging words in a dataset
d) By relying on human input for responses
Answer: a) By using deep learning and transformers

37. Which AI tool can create realistic voiceovers?
a) Voicebox
b) Gemini API
c) OpenAI Codex
d) Canva
Answer: a) Voicebox

38. How can companies prevent Generative AI from spreading false information?
a) Implement AI-generated content verification systems
b) Limit AI-generated content
c) Disable AI usage
d) Stop using neural networks
Answer: a) Implement AI-generated content verification systems

39. What does multimodal AI refer to?
a) AI that can process and generate multiple types of data (text, image, audio)
b) AI that only works with numerical data
c) AI that does not use neural networks
d) AI that processes data but does not generate content
Answer: a) AI that can process and generate multiple types of data (text, image, audio)

40. What is the main application of LLaMA models?
a) Text generation and NLP tasks
b) Image classification
c) Self-driving cars
d) Spam detection
Answer: a) Text generation and NLP tasks

41. Why is transparency important in AI-generated content?
a) To ensure accountability and credibility
b) To make AI processing slower
c) To reduce the number of AI-generated texts
d) To make it easier for AI to generate responses
Answer: a) To ensure accountability and credibility

42. What is an effective way to detect deepfake videos?
a) AI-based detection tools
b) Manual checking only
c) Avoiding all online videos
d) Using image compression techniques
Answer: a) AI-based detection tools

43. Why is data diversity important in AI model training?
a) To reduce bias and improve accuracy
b) To decrease the model’s computational power
c) To make AI models work slower
d) To prevent AI from generating text
Answer: a) To reduce bias and improve accuracy

44. What is a key benefit of AI-generated content in business?
a) Faster content creation for marketing
b) Eliminates the need for human employees
c) Increases hardware costs
d) Reduces the internet’s processing speed
Answer: a) Faster content creation for marketing

45. How can Generative AI improve customer service?
a) By creating AI-powered chatbots for instant responses
b) By eliminating the need for customer support teams
c) By generating long wait times
d) By reducing the number of customers
Answer: a) By creating AI-powered chatbots for instant responses

46. What is an example of AI-generated plagiarism?
a) Using AI-generated text without attribution
b) Writing essays by hand
c) Using a calculator to solve math problems
d) Watching online tutorials
Answer: a) Using AI-generated text without attribution

47. Which AI tool can generate realistic human-like conversations?
a) Claude 3.5
b) Canva
c) Microsoft Excel
d) Tableau
Answer: a) Claude 3.5

48. What should be done to ensure ethical AI usage in the workplace?
a) Implement AI guidelines and transparency policies
b) Ban all AI tools
c) Reduce AI model training
d) Remove AI from the hiring process
Answer: a) Implement AI guidelines and transparency policies

49. What is one concern of using Generative AI in news writing?
a) The spread of misinformation
b) Faster reporting
c) Increased reader engagement
d) Lower writing costs
Answer: a) The spread of misinformation

50. How can AI-generated images be distinguished from real ones?
a) Using AI detection software
b) Analyzing the image manually
c) Avoiding online images
d) Decreasing screen brightness
Answer: a) Using AI detection software

1. What is Generative AI, and how does it work?

Answer:
Generative AI is a branch of artificial intelligence that creates new content, such as text, images, audio, and video, based on training data. It learns patterns from existing datasets using machine learning models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These models analyze data and generate new samples that resemble the training data, making AI capable of creative tasks like content writing, music composition, and deepfake creation.

2. How do Generative AI models differ from Discriminative AI models?

Answer:
Generative AI models aim to learn the data distribution and generate new content, while Discriminative AI models focus on distinguishing between different classes of data.

Generative models: Create new data samples (e.g., GANs, VAEs, LLMs).
Discriminative models: Classify data into predefined categories (e.g., Logistic Regression, SVM, Decision Trees).
For example, Generative AI can create an image of a cat, while Discriminative AI can classify whether an image contains a cat or not.

3. What are Generative Adversarial Networks (GANs) and how do they function?

Answer:
GANs are a type of deep learning model consisting of two neural networks:

Generator: Creates new data samples that resemble real data.
Discriminator: Evaluates and differentiates between real and generated data.
These two networks compete with each other in an adversarial process. Over time, the Generator improves, producing increasingly realistic outputs. GANs are widely used for image generation, deepfakes, and style transfer.

4. What is a Variational Autoencoder (VAE), and how is it used in Generative AI?

Answer:
A Variational Autoencoder (VAE) is a neural network used in Generative AI to learn data representations efficiently. It consists of:

Encoder: Compresses input data into a latent space (a condensed representation).
Decoder: Reconstructs the original data from this latent space.
Unlike GANs, VAEs focus on capturing the underlying structure of data rather than just generating realistic images. They are useful in tasks like image synthesis, anomaly detection, and missing data reconstruction.

5. What are Large Language Models (LLMs), and how do they contribute to Generative AI?

Answer:
Large Language Models (LLMs) are deep learning models trained on massive text datasets to perform various Natural Language Processing (NLP) tasks. They can generate human-like text, translate languages, answer questions, and assist in creative writing. Examples include GPT-4, Gemini, and Claude 3.5. LLMs use transformer architectures, enabling them to process and understand contextual relationships in text efficiently.

6. How do transformers improve the performance of Large Language Models (LLMs)?

Answer:
Transformers are neural network architectures designed to process large sequences of text efficiently. They use mechanisms like:

Self-attention: Allows the model to focus on important words in a sentence while generating output.
Positional encoding: Helps retain word order information.
Parallel processing: Improves speed by handling multiple words simultaneously.
These features enable LLMs like GPT and Gemini to generate coherent and contextually relevant text.

7. What are some real-world applications of Generative AI?

Answer:
Generative AI has diverse applications, including:

Image Generation: Tools like DALL-E create realistic images from text prompts.
Text Generation: ChatGPT and Gemini generate human-like responses in conversations.
Audio Generation: AI models like Voicebox generate synthetic voices and music.
Video Creation: AI tools like Lumiere generate videos based on text descriptions.
Medical Research: AI-generated synthetic data aids in disease prediction and diagnosis.

8. What are deepfakes, and why are they considered a major ethical concern?

Answer:
Deepfakes are AI-generated videos or images that manipulate real visuals to create highly realistic yet fake content. They use GANs to replace faces, voices, or actions in media. Ethical concerns include:

Misinformation and Fraud: Deepfakes can spread false information, leading to public deception.
Privacy Violations: Fake videos can damage personal reputations.
Political Manipulation: Altered media can influence elections and public opinion.
To counteract deepfakes, AI detection tools and strict regulations are essential.

9. How can biases in Generative AI models be addressed?

Answer:
Bias in Generative AI arises when models are trained on imbalanced or non-representative data. Solutions include:

Diverse Training Data: Ensuring datasets include varied demographics and perspectives.
Algorithm Auditing: Regularly evaluating AI outputs for unintended biases.
Human Oversight: Involving human reviewers to assess AI-generated content.
Transparency: Making AI decision-making processes understandable and open to scrutiny.

10. What is the Gemini API, and how can it be used to create AI-powered chatbots?

Answer:
The Gemini API is an AI-powered tool developed by Google that allows developers to create advanced chatbots. Steps to build a chatbot using Gemini API:

Obtain an API Key: Register on Google AI Studio and acquire an API key.
Set Up Python Environment: Install the Gemini API package.
Initialize the API: Connect to the Gemini model using authentication.
Develop a Chat System: Implement user interactions using generative AI.
Deploy the Chatbot: Use it for customer support, education, or research applications.

11. What are some limitations of Generative AI?

Answer:
Despite its capabilities, Generative AI has limitations:

High Computational Cost: Requires powerful hardware for processing.
Data Dependency: Quality depends on the training dataset.
Bias and Misinformation: Can generate false or biased information.
Lack of Originality: AI-generated content may lack genuine creativity.
To improve AI-generated outputs, continuous model refinement and ethical AI practices are necessary.

12. What are the risks associated with Large Language Models (LLMs)?

Answer:
Key risks of LLMs include:

Data Privacy Issues: Models may memorize and leak sensitive data.
Bias and Ethical Concerns: AI-generated content may reflect societal biases.
Misinformation: AI can produce incorrect but confident-sounding answers.
Security Threats: AI models can be exploited for cybercrimes and deepfake scams.
Proper regulation, transparency, and ethical AI development can help mitigate these risks.

13. What ethical considerations should be made when using Generative AI in content creation?

Answer:

Plagiarism Prevention: Clearly label AI-generated content and credit original authors.
Data Transparency: Disclose training data sources to ensure credibility.
Regulatory Compliance: Follow copyright laws and fair usage policies.
Bias Mitigation: Ensure content does not perpetuate stereotypes or discrimination.

14. How is Generative AI used in personalized marketing and advertising?

Answer:
Generative AI helps in:

Content Generation: AI creates personalized ad copy and visuals.
Customer Interaction: Chatbots assist customers with tailored recommendations.
A/B Testing: AI optimizes ad campaigns by generating multiple variations.
Predictive Analytics: AI analyzes consumer behavior for targeted marketing.

15. How can AI-generated media be identified and prevented from spreading misinformation?

Answer:

AI Detection Tools: Use deepfake detection software.
Watermarking: Mark AI-generated content with metadata.
Fact-Checking: Cross-check AI-generated information with credible sources.
Public Awareness: Educate users on identifying AI-generated misinformation.

1. Assertion (A): Generative AI can create new and unique content, such as text, images, and music.

Reason (R): Generative AI models, such as GANs and VAEs, learn patterns from existing data to generate similar but new content.

a) Both A and R are true, and R is the correct explanation of A.
b) Both A and R are true, but R is not the correct explanation of A.
c) A is true, but R is false.
d) A is false, but R is true.
Answer: a) Both A and R are true, and R is the correct explanation of A.

Explanation: Generative AI models analyze training data and generate new outputs that resemble it. Techniques like GANs and VAEs allow AI to create realistic images, text, and music by learning the structure of input data.

2. Assertion (A): Large Language Models (LLMs) like GPT-4 and Gemini are trained on vast amounts of text data to generate human-like responses.

Reason (R): LLMs use supervised learning only to process and generate text.

Explanation: While LLMs like GPT-4 and Gemini are trained on vast text datasets, they primarily use unsupervised learning and transformer-based architectures, not just supervised learning, to understand and generate text.

3. Assertion (A): Deepfake technology can be used to create highly realistic fake videos.

Reason (R): Deepfakes use discriminative models to generate realistic images and videos.

Explanation: Deepfake technology relies on Generative Adversarial Networks (GANs), which are generative models, not discriminative ones. GANs generate realistic media by refining outputs through adversarial training.

4. Assertion (A): Generative AI can sometimes generate biased or misleading information.

Reason (R): The training data used for AI models may contain biases, which the AI can learn and replicate.

Explanation: AI models learn from large datasets that may include historical biases. If these biases exist in the training data, the AI model may unintentionally generate biased or misleading content.

5. Assertion (A): AI-generated text and images must always be credited to the AI model that created them.

Reason (R): AI-generated content is legally recognized as having independent intellectual property rights.

Explanation: While AI-generated content should be credited to maintain ethical transparency, AI does not have independent intellectual property rights. Currently, copyright laws attribute ownership to the user or company that developed and trained the AI, not the AI itself.

Competency Based Questions

1. Scenario:

A digital marketing agency wants to automate content creation for its clients using Generative AI. They plan to use AI for generating promotional images, writing social media posts, and even creating short video advertisements.

Question:

What are some advantages and ethical concerns the agency should consider while using Generative AI for content creation?

Answer:

Advantages:

Efficiency: AI can generate high-quality content quickly, reducing manual effort.
Cost-effectiveness: Automating content creation reduces the need for large creative teams.
Customization: AI can tailor content for different audiences based on data analysis.

Ethical Concerns:

Plagiarism and Copyright Issues: AI-generated content may resemble existing copyrighted material.
Bias in Content: If AI is trained on biased data, it may produce misleading or unfair content.
Misinformation: AI can generate fake or misleading ads, harming brand reputation.
To mitigate risks, the agency should disclose AI use, verify content authenticity, and ensure compliance with copyright laws.

2. Scenario:

A news organization is considering using a Large Language Model (LLM) like ChatGPT to generate automated news reports based on real-time data.

Question:

How can the news organization ensure that AI-generated news articles are accurate, unbiased, and ethical?

Answer:

To ensure responsible AI-generated news, the organization should:

Use Verified Sources: Train AI models on credible, fact-checked news databases.
Human Review: Have journalists review AI-generated content before publishing.
Bias Detection: Implement AI bias detection tools to avoid misrepresentation.
Transparency: Clearly label AI-generated articles to inform readers.
Regular Updates: Continuously update training data to reflect current and diverse perspectives.

By following these steps, the organization can balance AI efficiency with journalistic integrity.

3. Scenario:

A startup is developing a healthcare chatbot using Generative AI to assist patients by providing symptom-based recommendations.

Question:

What are the potential benefits and risks of using Generative AI in healthcare, and how can the startup address these risks?

Answer:

Benefits:

24/7 Availability: Chatbots provide instant support anytime.
Cost Reduction: AI reduces the need for human agents for initial consultations.
Data-Driven Insights: AI can analyze patient interactions to suggest improvements.

Risks and Solutions:

Inaccuracy in Diagnoses: AI-generated recommendations should not replace medical professionals; the chatbot must clearly state its limitations.
Data Privacy Issues: Implement strong encryption and compliance with HIPAA/GDPR to protect patient data.
Bias in Medical Advice: Train AI on diverse, high-quality medical data to prevent biased recommendations.

By incorporating human oversight and secure AI practices, the chatbot can enhance healthcare accessibility without compromising patient safety.

4. Scenario:

A social media company is considering the use of Generative Adversarial Networks (GANs) to create personalized content recommendations and advertisements for users.

Question:

How can the company balance AI-driven personalization with ethical considerations such as privacy and misinformation?

Answer:

Balancing AI-driven personalization with ethics:

User Consent: Obtain explicit permission before collecting and analyzing user data.
Misinformation Control: Use fact-checking AI models to prevent false content from being recommended.
Bias-Free Advertising: Ensure AI models do not favor specific demographics unfairly.
Transparency: Allow users to opt-out of AI-based content recommendations.

By implementing these responsible AI guidelines, the company can enhance user experience while maintaining trust and compliance with ethical standards.

5. Scenario:

A university is introducing an AI-powered tool to help students generate academic essays and research summaries.

Question:

What strategies should the university implement to ensure AI is used responsibly in academic settings?

Answer:

To ensure ethical AI use in education, the university should:

AI Usage Guidelines: Clearly define when and how students can use AI tools.
Plagiarism Detection: Use AI-generated content detectors to prevent academic dishonesty.
Critical Thinking Emphasis: Encourage students to fact-check AI-generated summaries and use AI as a learning aid rather than a replacement for independent thought.
Citations and Transparency: Require students to cite AI sources when using AI-generated content in assignments.

ai cbse

This site is dedicated to provide contents, notes, questions bank,blogs,articles and other materials for AI students of CBSE.

UNIT 7: Generative AI

Why Math is the Secret Ingredient Behind AI and Data Science

Unit 3: Making Machines See – Computer Vision

Generative AI

ai cbse

Related posts

UNIT 6: Neural Network

UNIT 5: Introduction to Big Data and Data Analytics

Unit 3 – Maths For AI

Leave a Reply Cancel reply