AI Class 10 Project Cycle Questions (CBSE 417) are designed to help students understand and practice the key stages of the AI Project Cycle. Based on the CBSE curriculum, these questions cover problem scoping, data acquisition, data exploration, modeling, and evaluation, helping students strengthen concepts and perform well in examinations.
Study Project Cycle here before practising the Questions
MCQ’s
1. What is the purpose of defining the problem statement during the Problem Scoping stage in an AI project cycle?
- To collect data
- To understand the aim and objective of the project
- To train the model
- To process data
Answer
2) To understand the aim and objective of the project.
2. In what ways can AI models be categorized based on the type of data fed into them?
- Two domains
- Four domains
- Three domains
- Five domains
Answer
3) Three domains
3. In Statistical Data, what is the primary function of the system in relation to data?
- Generating large datasets
- Analyzing data to extract insights
- Converting data into images
- Distributing data across networks
Answer
2. Analyzing data to extract insights
4. What is the main goal of Computer Vision projects?
- Translating audio data into visual descriptions
- Converting digital data into analogue signals
- Teaching machines to understand textual information
- Converting digital visual data into computer-readable language
Answer
2. Converting digital data into analogue signals
5. What is the primary focus of NLP?
- Analyzing computer languages
- Interacting between computers and humans using artificial language
- Dealing with the interaction between computers and humans using natural language
- Enhancing human-to-human communication
Answer
C) Dealing with the interaction between computers and humans using natural language
6. What do frameworks provide in the context of problem-solving?
- Random solutions
- Step-by-step guidance
- Legal advice
- Ethical justifications
Answer
B) Step-by-step guidance
7. How are Ethical Frameworks for AI categorized?
- Into legal and illegal frameworks
- Into sector-based and value-based frameworks
- Into historical and contemporary frameworks
- Into theoretical and practical frameworks
Answer
B) Into sector-based and value-based frameworks
8. What is the central focus of virtue-based value-based frameworks?
- Maximizing utility
- Protecting human rights
- Aligning actions with ethical principles and beliefs
- Ensuring compliance with legal regulations
Answer
C) Aligning actions with ethical principles and beliefs
9. Which of the following best describes rights-based value-based frameworks?
- Prioritizing human rights and dignity, valuing human life over other considerations
- Evaluating actions based on maximizing overall good and minimizing harm
- Centering on the character of the decision-maker and the alignment of actions with personal or societal virtues
- Focusing on achieving outcomes that offer the greatest benefit
Answer
A) Prioritizing human rights and dignity, valuing human life over other considerations
10. What is the primary domain of application for Bioethics?
- Agriculture
- Healthcare and life sciences
- Information technology
- Environmental conservation
Answer
B) Healthcare and life sciences
11. How can you identify the problem scoping in a project?
- Understand why the project was started
- Define the project’s primary objectives
- Outline the project’s work statement
- All of the above
Answer
D) All of the above
12. For better efficiency of an AI project, training data should be _______
- Both i and ii
- Both i and iv
- Only i
- Only iv
Answer
B) Both i and iv
13. Which one of the following is the second stage of the AI Project Cycle?
- Data Exploration
- Data Acquisition
- Modelling
- Problem Scoping
Answer
B) Data Acquisition
14. Which of the following comes under Problem Scoping?
- System Mapping
- 4Ws Canvas
- Data Features
- Web scraping
Answer
B) 4Ws Canvas
15. The primary purpose of data exploration in the AI project cycle is:
- To make data more complicated
- To simplify complex data
- To discover patterns and insights in data
- To visualize data
Answer
C) To discover patterns and insights in data
16. How can you identify the problem scoping in a project?
- Understand why the project was started
- Define the project’s primary objectives
- Outline the project’s work statement
- All of the above
Answer
D) All of the above
17. __________ summarizes all of the important points in one place.
- Problem statement template
- Problem statement document
- Problem statement files
- None of the above
Answer
A) Problem statement template
18. You would feed the data into the machine. This is the data with which the machine can be trained. Once it is ready, it will predict new data efficiently. This previous data is known as:
- Testing Data
- Training Data
- Exploring Data
- All of the above
Answer
B) Training Data
Assertion & Reasoning
1. Assertion–Reason Question
- Both Assertion and Reasoning are true, and Reasoning is the correct explanation of the Assertion.
- Assertion is true, but Reasoning is false.
- Both Assertion and Reasoning are true, but Reasoning is not the correct explanation of the Assertion.
- Assertion is false, but Reasoning is true.
Answer
A) Both Assertion and Reasoning are true, and Reasoning is the correct explanation of the Assertion.
2. Assertion–Reason Question
- Both Assertion and Reasoning are true, and Reasoning is the correct explanation of the Assertion.
- Assertion is true, but Reasoning is false.
- Both Assertion and Reasoning are true, but Reasoning is not the correct explanation of the Assertion.
- Assertion is false, but Reasoning is true.
Answer
A) Both Assertion and Reasoning are true, and Reasoning is the correct explanation of the Assertion.
Short Answer Question’s (SAQ’s)
1. What is the major role of an AI Project Cycle?
Answer
The major role of AI Project Cycle is to distribute the development of AI project in various stages so that the development becomes easier, clearly understandable and the steps / stages should become more specific to efficiently get the best possible output.
2. What roles does computer vision play in agricultural monitoring systems?
Answer
Computer vision is employed in agriculture for crop monitoring, pest detection, and yield estimation. Drones equipped with cameras capture aerial images of farmland, which are then analysed to assess crop health and optimise farming practices.
3. Mention the factors which knowingly or unknowingly influence our decision-making.
Answer
- Value of humans and value of non-humans
- Religion: Is the decision I am taking aligned with my religious views?
- Intuition and values: Does what I am thinking sound correct?
- Identity of the charity recipient
- Location of the recipient
- Bias towards relatives
- Availability of information
4. What is the necessity for Ethical Frameworks in AI development?
Answer
Ethical frameworks in AI development are necessary to ensure that AI systems are safe, fair, trustworthy, accountable, and aligned with human values. They act as guardrails in an evolving technological landscape, helping developers, organisations, and governments guide AI towards socially beneficial outcomes.
By using ethical frameworks while building AI solutions, we can prevent unintended consequences and ensure that AI systems make morally acceptable and responsible decisions.
5. Mention the key characteristics of sector-based frameworks.
Answer
Sector-based AI frameworks are customised, practical, and context-aware guidelines that bridge general ethical principles with real-world applications. They help ensure that AI systems are safe, fair, and effective within their specific domains of use.
These frameworks address sector-specific issues such as patient privacy, data security, and the ethical use of AI in medical decision-making.
6. What do you mean by Bioethics?
Answer
Bioethics is an ethical framework used in healthcare and life sciences. It deals with ethical issues related to health, medicine, and biological sciences, ensuring that AI applications in healthcare adhere to ethical standards and considerations.
Bioethics helps society navigate complex ethical challenges in medicine and life sciences by promoting responsible and humane practices in the face of technological and scientific progress.
7. What are the three categories of Value-based Frameworks?
Answer
- Rights-based: Prioritises the protection of human rights and dignity, valuing human life over other considerations.
- Utility-based: Evaluates actions based on maximising overall good, aiming to achieve the greatest benefit while minimising harm.
- Virtue-based: Focuses on the character and intentions of individuals involved in decision-making.
8. How do value-based frameworks contribute to ethical decision-making by emphasizing fundamental principles and values?
Answer
Value-based frameworks centre around core moral values such as:
- Autonomy: Respecting individuals’ rights to make informed choices
- Justice: Ensuring fairness, equality, and non-discrimination
- Beneficence: Promoting well-being and positive outcomes
- Non-maleficence: Avoiding harm
- Accountability: Taking responsibility for decisions and outcomes
- Transparency: Making AI processes understandable and traceable
They contribute to ethical AI decision-making by embedding moral values into design and policy, offering principled guidance for complex choices, and supporting human-centric, fair, and responsible AI development. These frameworks ensure that AI not only functions correctly but also does the right thing.
9. What are the fundamental principles of Ethical Frameworks?
Answer
- Fairness: Avoiding bias and ensuring equitable treatment
- Transparency: Making AI systems understandable and explainable
- Accountability: Ensuring responsibility for AI outcomes
- Privacy and Data Protection
- Human-Centeredness: Keeping human welfare and autonomy at the core
- Safety and Security: Preventing unintended harm
10. How is Natural Language Processing used in Machine Translation?
Answer
Natural Language Processing (NLP) is used in machine translation systems such as Google Translate and Microsoft Translator to automatically translate text from one language to another. These systems analyse the structure and meaning of sentences in the source language and generate accurate translations in the target language.
Long Answer Questions (LAQ’s)
1. A global company deploys an AI system to automate its recruitment process. The system is trained on historical hiring data to screen and rank job applicants. After several months, internal audits reveal that the AI consistently ranks male applicants higher than equally qualified female and minority candidates. The HR team is now under pressure to either fix the bias or shut down the system. The company wants to use an ethical framework to guide its next steps. Using an ethical AI framework, how should the company evaluate and respond to the bias in its AI recruitment system? Identify the ethical principles involved and propose a suitable course of action
Answer
a. Human Agency and Oversight
Issue: Automated decisions are made without sufficient human review.
Action: Reintroduce human oversight in final hiring decisions, especially for flagged or borderline candidates.
b. Technical Robustness and Safety
Issue: The model lacks fairness and accuracy across demographics.
Action: Audit and retrain the AI with diverse, balanced, and inclusive data, and test for bias using fairness metrics.
c. Privacy and Data Governance
Issue: Personal applicant data is used for algorithmic profiling.
Action: Ensure transparent data use policies and compliance with privacy laws (e.g., GDPR).
d. Transparency
Issue: Candidates and even HR staff are unclear on how decisions are made.
Action: Use explainable AI tools to show how and why the model ranks candidates.
2. Akhil wants to learn how to scope the problem for an AI Project. Explain him the following: (a) 4W Problem Canvas (b) Problem Statement Template
Answer
The 4Ws Problem canvas helps in identifying the key elements related to the problem. The 4Ws are Who, What, Where and Why
- The “Who” block helps in analysing the people getting affected directly or indirectly due to the problem.
- The “What” block helps us to determine the nature of the problem.
- The “Where” block helps us to look into the situation in which the problem arises, the context of it, and the locations where it is prominent.
- The “Why” block suggests to us the benefits which the stakeholders would get from the solution and how it will benefit them as well as the society
Our
[stakeholders]
Who
Have a problem that
[need]
What
When/while
[context/ location/ situation]
Where
An ideal solution would be
[solution]
Why
3. A city government wants to implement a computer vision-based traffic monitoring system to detect traffic violations like running red lights and illegal parking. The system uses real-time video feeds and AI to automatically identify license plates and send violation notices. What are the key ethical considerations for successfully deploying this computer vision application, and how can these challenges is addressed?
Answer
1. Privacy and Surveillance Concerns
Issue: Constant monitoring may violate citizens’ privacy.
Solution: Anonymize individuals in video feeds and encrypt license plate data, limiting access only to authorized personnel.
2. Data Retention and Use
Issue: Misuse or over-retention of surveillance data.
Solution: Set clear data retention policies and delete data once its intended purpose is fulfilled, unless legally required otherwise.
3. Bias and Fairness
Issue: Algorithmic bias may disproportionately flag vehicles in certain areas or with certain license styles.
Solution: Audit training data for representativeness and ensure fair distribution of detection accuracy across demographics and vehicle types.
4. Transparency and Accountability
Issue: Citizens may not understand how or why they were flagged.
Solution: Provide public documentation of how the system works and establish a dispute resolution process for incorrect fines.
4. A customer service company wants to implement an NLP-based chatbot to handle client inquiries 24/7. The goal is to reduce human workload, improve response time, and increase customer satisfaction. However, users speak different languages, use slang, and sometimes express frustration or sarcasm.
Answer
1.
Transparency
Issue: Customers may feel misled if they think they’re talking to a human.
Solution: Clearly disclose at the beginning that they are interacting with an AI system.
2.
Bias and Fairness
Issue: The bot may reflect biased training data and respond inappropriately to certain user groups.
Solution: Audit and test the system using diverse inputs to minimize bias, and use inclusive training datasets that represent different cultures, accents, and language use.
3.
Data Privacy
Issue: The bot handles sensitive customer information.
Solution: Encrypt conversations, comply with data privacy laws (e.g., GDPR, CCPA), and limit data retention to what’s necessary.
4.
User Trust and Experience
Issue: Poor or robotic responses may frustrate users.
Solution: Train the bot to use natural, empathetic language and gather feedback through surveys to continuously improve interaction quality.
- Use robust multilingual and sentiment-aware NLP models
- Ensure ethical practices, including transparency, fairness, and data protection
- Design with the user experience in mind, allowing seamless escalation and empathetic dialogue
5. A hospital implements an AI diagnostic tool that analyzes medical images (e.g., X-rays, MRIs) to assist doctors in detecting diseases like cancer. The AI has shown high accuracy in clinical trials, but a recent case revealed that it missed a rare condition, leading to a delayed diagnosis and patient harm. Using bioethical principles (autonomy, beneficence, non-maleficence, and justice), evaluate the ethical implications of deploying such AI systems in clinical settings. What measures should be taken to ensure that AI in healthcare aligns with bioethical standards?
Answer
Bioethical Principles Applied:
1. Autonomy
Issue: Patients may not be informed that AI is involved in their diagnosis.
Action: Ensure informed consent, where patients are made aware of AI usage and have the right to ask for human-only analysis.
2. Beneficence (Do Good)
Issue: AI is intended to enhance diagnosis and reduce errors.
Action: Continuously improve the model’s performance and ensure it’s used to support, not replace, clinicians.
3. Non-Maleficence (Do No Harm)
Issue: A missed diagnosis caused real patient harm.
Action: Set clear protocols for human oversight; use AI only as a second opinion, not the sole source of decision-making.
4. Justice
Issue: AI might perform poorly for certain demographics (e.g., rare conditions, underrepresented groups).
Action: Audit the model for bias and equity, and expand training data to include diverse patient populations.
- Implement explain ability tools so doctors can understand AI decisions.
- Require dual validation—AI + physician—before acting on diagnoses.
- Set up ethics review boards to evaluate AI tools before deployment.
- Create a feedback loop to learn from errors and update the system regularly.
- Provide transparency reports to patients and regulators about AI limitations.
This scenario underscores the importance of bioethics in AI applications for healthcare. While AI offers enormous potential, adherence to the principles of autonomy, beneficence, non-maleficence, and justice is essential to ensure safe, ethical, and equitable patient care.