CBSE Class 9 AI Reflection, Project Cycle & Ethics Important Subjective Questions & Answers (2026–27) | Unit 1 Artificial Intelligence
Class 9 · Artificial Intelligence
CBSE Class 9 AI Reflection, Project Cycle & Ethics Important Subjective Questions & Answers (2026–27)
Prepare for your CBSE Class 9 Artificial Intelligence examination with these 25 important subjective questions and answers from Unit 1: AI Reflection, Project Cycle and Ethics. These questions are prepared according to the latest CBSE syllabus (2026–27) and are ideal for Unit Tests, Half-Yearly, Annual Examinations, competency-based questions, and viva preparation.
Topics Covered
- Introduction to Artificial Intelligence
- Domains of Artificial Intelligence
- Applications of AI
- AI Project Cycle
- Problem Scoping
- Data Acquisition
- Data Exploration
- Modeling, Evaluation and Deployment
- Artificial Intelligence Ethics
- Bias, Privacy and Human Rights
CBSE Exam Tip: Focus on definitions, differences, AI Project Cycle stages, 4Ws Problem Canvas, AI domains, and ethical principles. Draw neat diagrams wherever applicable and underline important keywords to score maximum marks.
Subjective Questions with Answers
Q1. Define Artificial Intelligence (AI).
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Answer:
Artificial Intelligence (AI) is the ability of a machine or computer system to imitate human intelligence. It enables machines to learn from data, make decisions, solve problems, recognize patterns, and improve their performance through experience.
AI is used in various fields such as healthcare, education, banking, agriculture, transportation, and entertainment.
Q2. Explain the "Three-Strand Braid" analogy used to describe Artificial Intelligence.
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Answer:
The "Three-Strand Braid" explains that Artificial Intelligence is made up of three important domains that work together:
- Data Science (Statistical Data) – Works with numbers and data to identify patterns.
- Natural Language Processing (NLP) – Enables computers to understand and communicate using human language.
- Computer Vision (CV) – Helps machines understand and analyze images and videos.
Together, these three domains form the foundation of Artificial Intelligence.
Q3. Differentiate between the three domains of Artificial Intelligence.
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| Domain | Type of Data Used | Main Purpose |
|---|---|---|
| Data Science | Numerical and statistical data | Finds patterns and predicts future outcomes. |
| Natural Language Processing (NLP) | Text and speech | Helps computers understand and generate human language. |
| Computer Vision | Images and videos | Enables machines to identify and understand visual information. |
Q4. Describe how Computer Vision is used in the medical field.
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Answer:
Computer Vision is widely used in healthcare to analyze medical images such as X-rays, CT scans, and MRI scans. It helps doctors detect diseases, create detailed 3D images of body organs, identify abnormalities, and improve the accuracy of diagnosis and treatment.
Q5. How is Data Science used for fraud and risk detection in banking?
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Answer:
Data Science analyzes large amounts of customer data, transaction history, and spending patterns. By identifying unusual activities and calculating risk, banks can detect fraudulent transactions, reduce financial losses, and provide suitable financial products to customers.
Q6. What is the AI Project Cycle? Why is it important?
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Answer:
The AI Project Cycle is a systematic process used to develop Artificial Intelligence solutions. It consists of six stages:
- Problem Scoping
- Data Acquisition
- Data Exploration
- Modeling
- Evaluation
- Deployment
It is important because it organizes the development process, makes complex problems easier to solve, and improves the accuracy and efficiency of AI solutions.
Q7. Define Problem Scoping and list its main steps.
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Answer:
Problem Scoping is the first stage of the AI Project Cycle in which the problem is identified clearly and a goal is defined.
The main steps are:
- Identify the Theme.
- Select the Topic.
- Identify the Problem.
- Define the Goal.
Q8. Explain the 4Ws Problem Canvas used in Problem Scoping.
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Answer:
The 4Ws Problem Canvas helps understand the problem from different perspectives.
- Who – Who is affected by the problem?
- What – What is the exact problem?
- Where – Where does the problem occur?
- Why – Why is solving this problem important?
It helps developers clearly define the problem before building an AI solution.
Q9. Who are stakeholders in an AI project?
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Answer:
Stakeholders are the people or groups who are directly or indirectly affected by the problem and who will benefit from the AI solution. They may include users, customers, organizations, developers, and the community.
Q10. What is the purpose of a Problem Statement Template?
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Answer:
A Problem Statement Template summarizes the information collected through the 4Ws Problem Canvas into one clear statement. It helps the development team understand the problem, remain focused on the project goal, and design an effective AI solution.
Q11. Differentiate between Training Data and Testing Data.
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Answer:
| Training Data | Testing Data |
|---|---|
| Used to train the AI model. | Used to test the trained AI model. |
| Helps the model learn patterns and relationships. | Checks the accuracy and performance of the model. |
| Provided during the learning stage. | Used after the learning stage is completed. |
Q12. What are Data Features in an AI project?
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Answer:
Data Features are the characteristics or attributes of data that help an AI model understand and solve a problem.
For example, in a crop disease detection project, features may include crop type, soil moisture, temperature, humidity, and images of infected plants.
Q13. Explain the concept of System Maps.
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Answer:
A System Map is a visual representation that shows the relationship between different factors involved in a problem. It helps developers understand how one factor affects another and identifies the important variables needed to build an AI solution.
Q14. How are relationships represented in a System Map?
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Answer:
Relationships in a System Map are represented using arrows and symbols.
- Plus (+) sign shows a direct relationship. If one factor increases, the other also increases.
- Minus (−) sign shows an inverse relationship. If one factor increases, the other decreases.
Q15. Name any four common methods of acquiring data.
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Answer:
Four common methods of data acquisition are:
- Web Scraping
- Surveys and Questionnaires
- Sensors and IoT Devices
- Government Open Data Portals
Q16. What is the importance of Data Exploration?
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Answer:
Data Exploration is the process of analyzing and visualizing collected data to understand patterns, trends, and relationships. It helps developers identify useful information, detect errors, and choose the most suitable AI model.
Q17. Why is Data Visualization important in Artificial Intelligence?
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Answer:
Data Visualization presents information using charts, graphs, and diagrams, making complex data easier to understand. It helps identify hidden patterns, trends, and relationships, enabling developers to make better decisions while building AI models.
Q18. Compare Rule-Based and Learning-Based approaches in AI Modeling.
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| Rule-Based Approach | Learning-Based Approach |
|---|---|
| The developer writes predefined rules. | The machine learns rules automatically from data. |
| Suitable for simple and fixed problems. | Suitable for complex problems involving patterns. |
| Less flexible. | More flexible and continuously improves with data. |
Q19. Define the Evaluation stage of the AI Project Cycle.
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Answer:
Evaluation is the stage where the trained AI model is tested using testing data to measure its accuracy and performance. Different models are compared, and the model that produces the best results is selected for deployment.
Q20. What is Deployment in the AI Project Cycle?
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Answer:
Deployment is the final stage of the AI Project Cycle in which the trained and tested AI model is made available for real-world use. It may be integrated into websites, mobile applications, software systems, or smart devices so that users can benefit from the AI solution.
Q21. Differentiate between Morals and Ethics.
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Answer:
| Morals | Ethics |
|---|---|
| Morals are beliefs and values developed by society, culture, religion, or family. | Ethics are principles that help people decide what is right or wrong in different situations. |
| They are based on personal or social beliefs. | They guide professional and responsible behaviour. |
| Example: Always speak the truth. | Example: Protect users' personal information while developing AI systems. |
Q22. What is AI Bias? Why is it a concern?
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Answer:
AI Bias occurs when an AI system gives unfair or prejudiced results because of biased or incomplete training data.
It is a concern because it can lead to discrimination against certain individuals or groups based on gender, age, race, language, or other factors. Therefore, AI systems should always be trained using fair, balanced, and representative data.
Q23. Explain the ethical principle of Inclusion in Artificial Intelligence.
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Answer:
Inclusion means that AI systems should benefit everyone without discrimination. AI solutions should be designed so that people of different ages, genders, abilities, cultures, and economic backgrounds can use them equally.
Inclusive AI ensures fairness and equal opportunities for all members of society.
Q24. Why is Data Privacy an important ethical principle in Artificial Intelligence?
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Answer:
AI systems collect and process large amounts of personal data. Data Privacy ensures that this information is collected with the user's consent, stored securely, and used only for authorized purposes.
Protecting privacy helps prevent misuse of personal information, identity theft, and unauthorized access to sensitive data.
Q25. How can Artificial Intelligence affect Human Rights?
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Answer:
Artificial Intelligence can positively improve healthcare, education, and public services. However, if used irresponsibly, it may violate human rights by:
- Discriminating against certain groups.
- Violating privacy.
- Reducing employment opportunities through automation.
- Limiting freedom and fairness in decision-making.
Therefore, AI should always be developed and used ethically to protect human rights.
❓ Quick Revision
- Artificial Intelligence (AI) enables machines to perform tasks that normally require human intelligence.
- The three major domains of AI are Data Science, Natural Language Processing (NLP), and Computer Vision (CV).
- The six stages of the AI Project Cycle are Problem Scoping, Data Acquisition, Data Exploration, Modeling, Evaluation, and Deployment.
- The 4Ws Problem Canvas includes Who, What, Where, and Why.
- Training Data is used to teach the AI model, while Testing Data checks its performance.
- System Maps show relationships between different variables using '+' and '−' signs.
- Rule-Based systems follow predefined rules, whereas Learning-Based systems learn from data.
- AI Ethics focuses on fairness, transparency, accountability, privacy, and inclusion.
- AI Bias results from biased or unbalanced training data.
- Responsible AI development protects privacy, inclusion, fairness, and human rights.
? Exam Tip
For the CBSE Class 9 Artificial Intelligence examination, remember the definitions of AI, AI domains, the six stages of the AI Project Cycle, the 4Ws Problem Canvas, Training Data vs Testing Data, Rule-Based vs Learning-Based approaches, and the ethical principles of AI such as fairness, inclusion, privacy, and accountability. Draw simple tables for differences and underline important keywords to score full marks in subjective questions.