Unit 1: AI Project Cycle & Ethical Frameworks
Unit 1
Revisiting AI Project Cycle (35 MCQs with Answers)
MCQs
1. What is the first stage of the AI Project Cycle?
a) Data Acquisition
b) Problem Scoping
c) Data Exploration
d) Model Training
Answer: b) Problem Scoping
2. The main aim of the AI Project Cycle is to:
a) Develop games
b) Build intelligent systems to solve real-world problems
c) Replace human thinking
d) Avoid data collection
Answer: b) Build intelligent systems to solve real-world problems
3. Which of these steps comes after Problem Scoping in the AI Project Cycle?
a) Data Acquisition
b) Data Exploration
c) Model Training
d) Evaluation
Answer: a) Data Acquisition
4. Data exploration is also called:
a) Data Collection
b) Data Visualization and Analysis
c) Data Cleaning
d) Data Annotation
Answer: b) Data Visualization and Analysis
5. Which of the following best explains “Problem Scoping”?
a) Identifying and defining the problem clearly
b) Collecting raw data
c) Training the AI model
d) Testing the AI model
Answer: a) Identifying and defining the problem clearly
6. Data Acquisition means:
a) Collecting relevant data from different sources
b) Writing algorithms
c) Creating reports only
d) Avoiding unnecessary data
Answer: a) Collecting relevant data from different sources
7. Which of the following is used to train an AI model?
a) Raw unorganized data
b) Pre-processed structured data
c) Only images
d) Only audio files
Answer: b) Pre-processed structured data
8. Which is the correct sequence of stages in the AI Project Cycle?
a) Problem Scoping → Data Acquisition → Data Exploration → Model Training → Evaluation
b) Data Acquisition → Problem Scoping → Model Training → Evaluation
c) Model Training → Data Exploration → Problem Scoping → Evaluation
d) Problem Scoping → Model Training → Evaluation → Data Acquisition
Answer: a) Problem Scoping → Data Acquisition → Data Exploration → Model Training → Evaluation
9. The evaluation stage of the AI Project Cycle involves:
a) Testing the performance of the model
b) Collecting more raw data
c) Deciding project budget
d) Ignoring errors
Answer: a) Testing the performance of the model
10. What is “Bias” in the AI Project Cycle?
a) Equal treatment of all data
b) A systematic error in predictions caused by wrong or unbalanced data
c) Correct decision-making
d) A step in evaluation
Answer: b) A systematic error in predictions caused by wrong or unbalanced data
11. In Problem Scoping, stakeholders are:
a) Only data scientists
b) People or groups affected by the problem and solution
c) Only software developers
d) Only government officials
Answer: b) People or groups affected by the problem and solution
12. Which of these tools helps in Data Exploration?
a) Graphs and Charts
b) Tables
c) Maps
d) All of the above
Answer: d) All of the above
13. Data Acquisition can be done from:
a) Surveys
b) Databases
c) Sensors and online sources
d) All of the above
Answer: d) All of the above
14. Which of these is an example of Problem Scoping in AI?
a) Defining the problem of traffic congestion in a city
b) Cleaning the collected traffic data
c) Training a traffic prediction model
d) Evaluating the accuracy of the traffic model
Answer: a) Defining the problem of traffic congestion in a city
15. Data Pre-processing includes:
a) Removing missing values
b) Normalizing data
c) Handling outliers
d) All of the above
Answer: d) All of the above
16. Which of these is NOT a stage of AI Project Cycle?
a) Model Training
b) Data Cleaning
c) Data Acquisition
d) Advertising
Answer: d) Advertising
17. During Model Training, the system learns by:
a) Using training data and algorithms
b) Watching videos only
c) Listening to humans
d) Random guessing
Answer: a) Using training data and algorithms
18. Why is Evaluation important in the AI Project Cycle?
a) To check if the AI model works accurately
b) To stop the project suddenly
c) To avoid data collection
d) To make profits
Answer: a) To check if the AI model works accurately
19. If data used for training is incomplete, the AI system may give:
a) Accurate results
b) Biased or wrong results
c) Faster results
d) Free results
Answer: b) Biased or wrong results
20. Which visualization tool is most commonly used in Data Exploration?
a) Pie Chart
b) Bar Graph
c) Scatter Plot
d) All of the above
Answer: d) All of the above
21. “Garbage in, garbage out” in AI means:
a) Poor quality input data will lead to poor model output
b) AI can fix bad data automatically
c) Models can work without data
d) AI does not need training
Answer: a) Poor quality input data will lead to poor model output
22. Which step helps in understanding trends and patterns in data?
a) Problem Scoping
b) Data Acquisition
c) Data Exploration
d) Evaluation
Answer: c) Data Exploration
23. Which dataset is used to improve model accuracy after evaluation?
a) Testing data
b) Training data
c) Validation data
d) No dataset is required
Answer: c) Validation data
24. In AI projects, feedback loops are useful for:
a) Improving the model continuously
b) Avoiding data analysis
c) Replacing stakeholders
d) Reducing data storage
Answer: a) Improving the model continuously
25. Which of the following is a challenge in Data Acquisition?
a) High cost of collecting data
b) Data privacy issues
c) Availability of quality data
d) All of the above
Answer: d) All of the above
26. What does “generalization” mean in AI model training?
a) The ability of the model to perform well on new, unseen data
b) The ability to memorize training data
c) The ability to avoid evaluation
d) The ability to skip data acquisition
Answer: a) The ability of the model to perform well on new, unseen data
27. Which step of AI Project Cycle ensures that the project is solving the correct problem?
a) Problem Scoping
b) Data Exploration
c) Model Training
d) Evaluation
Answer: a) Problem Scoping
28. Why is data cleaning important before model training?
a) To improve accuracy of AI model
b) To reduce cost of hardware
c) To avoid stakeholders
d) To replace evaluation
Answer: a) To improve accuracy of AI model
29. Which of these is a type of data used in AI Project Cycle?
a) Structured data
b) Unstructured data
c) Semi-structured data
d) All of the above
Answer: d) All of the above
30. What happens if evaluation shows poor performance of AI model?
a) The project stops
b) The model is retrained with improved data
c) The data is ignored
d) The problem is abandoned immediately
Answer: b) The model is retrained with improved data
31. Which stage ensures “ethical use of AI” in the project cycle?
a) Problem Scoping
b) Data Acquisition
c) Evaluation
d) All stages should ensure ethics
Answer: d) All stages should ensure ethics
32. What is the role of algorithms in the AI Project Cycle?
a) To analyze and learn patterns from data
b) To replace raw data
c) To avoid training
d) To store data only
Answer: a) To analyze and learn patterns from data
33. The output of Data Exploration stage is usually in the form of:
a) Graphs, charts, and insights
b) Raw data only
c) Mathematical formulas
d) Evaluation results
Answer: a) Graphs, charts, and insights
34. Which stage decides the metrics for checking AI model success?
a) Data Exploration
b) Evaluation
c) Data Acquisition
d) Problem Scoping
Answer: b) Evaluation
35. Revisiting the AI Project Cycle is important because:
a) It ensures continuous improvement and correction of errors
b) It avoids stakeholder involvement
c) It removes the need of data
d) It skips training
Answer: a) It ensures continuous improvement and correction of errors
Topic: Stages of AI Project Cycle (20 MCQs with Answers)
MCQs
1. How many main stages are there in the AI Project Cycle?
a) 3
b) 4
c) 5
d) 6
Answer: c) 5
2. Which is the first stage of the AI Project Cycle?
a) Data Acquisition
b) Problem Scoping
c) Model Training
d) Evaluation
Answer: b) Problem Scoping
3. The stage where the problem is clearly defined is called:
a) Data Exploration
b) Problem Scoping
c) Evaluation
d) Model Training
Answer: b) Problem Scoping
4. Data Acquisition refers to:
a) Cleaning the data
b) Collecting relevant data from different sources
c) Creating a model
d) Testing a model
Answer: b) Collecting relevant data from different sources
5. The main aim of Data Exploration is to:
a) Train the model
b) Understand patterns and trends in the data
c) Collect raw data
d) Skip missing values
Answer: b) Understand patterns and trends in the data
6. In which stage does the AI system actually learn from data?
a) Problem Scoping
b) Data Acquisition
c) Model Training
d) Evaluation
Answer: c) Model Training
7. Evaluation stage of the AI Project Cycle checks:
a) Cost of the project
b) Performance and accuracy of the AI model
c) Sources of data
d) Stakeholders’ names
Answer: b) Performance and accuracy of the AI model
8. Which is the correct sequence of AI Project Cycle stages?
a) Data Acquisition → Problem Scoping → Model Training → Data Exploration → Evaluation
b) Problem Scoping → Data Acquisition → Data Exploration → Model Training → Evaluation
c) Model Training → Evaluation → Problem Scoping → Data Acquisition → Data Exploration
d) Problem Scoping → Data Exploration → Data Acquisition → Evaluation → Model Training
Answer: b) Problem Scoping → Data Acquisition → Data Exploration → Model Training → Evaluation
9. If a model is giving wrong predictions, which stage should be revisited?
a) Problem Scoping
b) Model Training
c) Evaluation
d) Any of the above
Answer: d) Any of the above
10. Which stage helps in identifying the needs of stakeholders?
a) Data Acquisition
b) Problem Scoping
c) Data Exploration
d) Evaluation
Answer: b) Problem Scoping
11. During Data Exploration, which tools are commonly used?
a) Charts and Graphs
b) Tables
c) Statistical summaries
d) All of the above
Answer: d) All of the above
12. “Garbage in, garbage out” is related to which stage?
a) Evaluation
b) Data Acquisition
c) Model Training
d) Problem Scoping
Answer: b) Data Acquisition
13. Model Training requires which type of dataset?
a) Cleaned and pre-processed data
b) Raw unstructured data
c) Only images
d) Only audio
Answer: a) Cleaned and pre-processed data
14. Which stage ensures the AI model is tested with new data?
a) Problem Scoping
b) Model Training
c) Data Exploration
d) Evaluation
Answer: d) Evaluation
15. Stakeholder involvement is most important in which stage?
a) Data Exploration
b) Problem Scoping
c) Model Training
d) Evaluation
Answer: b) Problem Scoping
16. Which of these is NOT a stage in AI Project Cycle?
a) Problem Scoping
b) Data Exploration
c) Deployment
d) Data Acquisition
Answer: c) Deployment
17. In which stage is data visualization most commonly used?
a) Data Exploration
b) Model Training
c) Problem Scoping
d) Evaluation
Answer: a) Data Exploration
18. The feedback loop in AI Project Cycle is useful for:
a) Re-scoping the problem
b) Retraining the model
c) Improving data collection
d) All of the above
Answer: d) All of the above
19. Which stage is also known as the “learning phase” of AI?
a) Data Acquisition
b) Model Training
c) Evaluation
d) Problem Scoping
Answer: b) Model Training
20. Why is it important to follow all stages of AI Project Cycle in order?
a) To save time and cost
b) To ensure systematic and accurate AI development
c) To avoid human workers
d) To skip testing
Answer: b) To ensure systematic and accurate AI development
Introduction to AI Domain (20 with Answers)
1. AI stands for:
a) Artificial Intelligence
b) Automated Information
c) Advanced Innovation
d) Artificial Interaction
Answer: a) Artificial Intelligence
2. The main goal of Artificial Intelligence is to:
a) Replace human workers completely
b) Make machines think and act like humans
c) Perform only calculations
d) Store large amounts of data
Answer: b) Make machines think and act like humans
3. Which of the following is NOT an application of AI?
a) Speech recognition
b) Machine translation
c) Handwriting recognition
d) Manual typewriting
Answer: d) Manual typewriting
4. AI is a branch of:
a) Mathematics
b) Computer Science
c) Physics
d) Biology
Answer: b) Computer Science
5. Which of the following is a common domain of AI?
a) Natural Language Processing (NLP)
b) Data Science
c) Computer Vision
d) All of the above
Answer: d) All of the above
6. AI enables machines to learn from:
a) Past experiences and data
b) Only humans
c) The internet only
d) Fixed instructions only
Answer: a) Past experiences and data
7. A self-driving car is an example of AI in the domain of:
a) Robotics and Computer Vision
b) Natural Language Processing
c) Gaming
d) Expert Systems
Answer: a) Robotics and Computer Vision
8. Which AI domain deals with understanding human language?
a) Expert Systems
b) Natural Language Processing
c) Robotics
d) Data Mining
Answer: b) Natural Language Processing
9. Which of the following is an example of AI in everyday life?
a) Washing clothes manually
b) Using Google Maps navigation
c) Writing with a pen
d) Manual calculators
Answer: b) Using Google Maps navigation
10. The ability of AI systems to “see” and recognize images is called:
a) Robotics
b) Machine Learning
c) Computer Vision
d) Neural Networking
Answer: c) Computer Vision
11. Which domain of AI uses knowledge bases to give advice or solutions?
a) Expert Systems
b) Natural Language Processing
c) Robotics
d) Machine Vision
Answer: a) Expert Systems
12. Which of these AI systems is used for medical diagnosis?
a) Robotics
b) Expert Systems
c) Computer Vision
d) Gaming
Answer: b) Expert Systems
13. Virtual assistants like Alexa and Siri are based on:
a) Robotics
b) Natural Language Processing
c) Computer Vision
d) Neural Networks
Answer: b) Natural Language Processing
14. Which domain of AI focuses on building machines that can perform physical tasks?
a) Robotics
b) NLP
c) Expert Systems
d) Data Science
Answer: a) Robotics
15. The main difference between AI and traditional programming is that AI:
a) Always follows fixed rules
b) Learns and improves with data
c) Cannot analyze patterns
d) Works without data
Answer: b) Learns and improves with data
16. Which AI application is used for detecting spam in emails?
a) Computer Vision
b) Robotics
c) Natural Language Processing
d) Expert Systems
Answer: c) Natural Language Processing
17. Which of these is NOT an AI domain?
a) Machine Learning
b) Deep Learning
c) Computer Graphics (only animation)
d) Natural Language Processing
Answer: c) Computer Graphics (only animation)
18. AI in gaming is used to:
a) Make games look attractive
b) Create smart opponents and environments
c) Replace human players
d) Reduce memory usage
Answer: b) Create smart opponents and environments
19. Which AI technology powers facial recognition systems?
a) Robotics
b) Computer Vision
c) NLP
d) Data Mining
Answer: b) Computer Vision
20. Which of the following best describes Artificial Intelligence?
a) Study of how humans learn
b) Simulation of human intelligence in machines
c) A programming language
d) A hardware component
Answer: b) Simulation of human intelligence in machines
Ethical Frameworks of AI (20 with Answers)
1. The term “ethics in AI” refers to:
a) Building faster AI machines
b) Making AI systems morally responsible and fair
c) Improving AI programming languages
d) Reducing AI costs
Answer: b) Making AI systems morally responsible and fair
2. Which of the following is an ethical concern in AI?
a) Data privacy
b) Fairness and bias
c) Job displacement
d) All of the above
Answer: d) All of the above
3. An AI system showing bias in hiring decisions is violating which principle?
a) Accountability
b) Fairness
c) Transparency
d) Efficiency
Answer: b) Fairness
4. Which ethical principle ensures that AI systems can be explained and understood by humans?
a) Privacy
b) Transparency
c) Security
d) Autonomy
Answer: b) Transparency
5. Data used to train AI should be:
a) Biased and incomplete
b) Ethical, diverse, and unbiased
c) Only numerical
d) Randomly selected
Answer: b) Ethical, diverse, and unbiased
6. If an AI system makes a wrong decision, which principle demands responsibility?
a) Accountability
b) Privacy
c) Autonomy
d) Transparency
Answer: a) Accountability
7. Which of the following is an example of AI violating privacy?
a) Facial recognition without consent
b) AI helping in medical diagnosis
c) AI chatbots assisting customers
d) AI in gaming
Answer: a) Facial recognition without consent
8. Which ethical principle is concerned with protecting user data in AI systems?
a) Transparency
b) Privacy
c) Fairness
d) Accountability
Answer: b) Privacy
9. The principle of non-maleficence in AI means:
a) AI should never cause harm to humans
b) AI should always be free
c) AI must be open source
d) AI should work fast
Answer: a) AI should never cause harm to humans
10. Ethical AI should aim for:
a) Safety and human well-being
b) Faster profit only
c) Maximum data collection
d) Elimination of human jobs
Answer: a) Safety and human well-being
11. Which framework ensures AI is aligned with human values?
a) Fairness
b) Human-centric AI
c) Transparency
d) Bias
Answer: b) Human-centric AI
12. AI showing discrimination in loan approval is an issue of:
a) Privacy
b) Accountability
c) Bias and fairness
d) Transparency
Answer: c) Bias and fairness
13. Which of the following is NOT an AI ethical principle?
a) Fairness
b) Accountability
c) Transparency
d) Speed of execution
Answer: d) Speed of execution
14. Which principle ensures that AI systems can be checked and monitored by humans?
a) Transparency
b) Autonomy
c) Privacy
d) Security
Answer: a) Transparency
15. AI replacing jobs is considered an ethical issue under:
a) Employment impact
b) Transparency
c) Privacy
d) Fairness
Answer: a) Employment impact
16. An AI trained only on male voices but failing on female voices is an example of:
a) Privacy issue
b) Transparency issue
c) Bias issue
d) Accountability issue
Answer: c) Bias issue
17. The principle of beneficence in AI means:
a) AI should provide benefits to society
b) AI should remain secret
c) AI should never collect data
d) AI should work without electricity
Answer: a) AI should provide benefits to society
18. Which organization has proposed global guidelines for ethical AI?
a) UNESCO
b) FIFA
c) NASA
d) WTO
Answer: a) UNESCO
19. The challenge of explainability in AI means:
a) Explaining AI decisions clearly to humans
b) Writing AI code faster
c) Reducing AI hardware size
d) Training AI with less data
Answer: a) Explaining AI decisions clearly to humans
20. Why is ethical AI important?
a) To make AI faster
b) To ensure AI benefits humans without causing harm
c) To replace human workers
d) To reduce internet usage
Answer: b) To ensure AI benefits humans without causing harm
Bioethics in AI (20 with Answers)
1. Bioethics refers to:
a) The study of biological systems only
b) Ethical issues in biology and medicine
c) The process of making robots
d) Computer programming in biology
Answer: b) Ethical issues in biology and medicine
2. Which of the following fields is MOST related to bioethics?
a) Artificial Intelligence in gaming
b) Genetic engineering and cloning
c) Online shopping
d) Space exploration
Answer: b) Genetic engineering and cloning
3. AI in healthcare raises bioethical concerns about:
a) Patient privacy and consent
b) Fast internet connection
c) Speed of computers
d) Electricity use
Answer: a) Patient privacy and consent
4. Which of the following is NOT a bioethical principle?
a) Autonomy
b) Beneficence
c) Justice
d) Profitability
Answer: d) Profitability
5. In bioethics, the principle of autonomy means:
a) Respecting patients’ right to make their own decisions
b) Doctors making all decisions without patients
c) Using AI to replace doctors
d) Selling medical data for money
Answer: a) Respecting patients’ right to make their own decisions
6. The use of AI in genetic testing raises concerns about:
a) Data security and misuse
b) The speed of testing
c) Entertainment use
d) Cost of mobile phones
Answer: a) Data security and misuse
7. Which principle in bioethics means “do no harm”?
a) Beneficence
b) Non-maleficence
c) Justice
d) Autonomy
Answer: b) Non-maleficence
8. An AI system misdiagnosing a patient due to biased data violates which principle?
a) Fairness and justice
b) Transparency
c) Autonomy
d) Profitability
Answer: a) Fairness and justice
9. The principle of justice in bioethics means:
a) Equal access to medical resources for all
b) Doctors only treating rich patients
c) AI replacing nurses
d) Faster computer systems in hospitals
Answer: a) Equal access to medical resources for all
10. Informed consent in bioethics means:
a) Taking patient permission before medical procedures
b) Forcing patients to accept treatment
c) Using AI without informing patients
d) Ignoring patient opinions
Answer: a) Taking patient permission before medical procedures
11. AI used in organ transplant matching must follow which bioethical principle?
a) Justice and fairness
b) Profitability
c) Random selection
d) Autonomy only
Answer: a) Justice and fairness
12. Which is an example of a bioethical issue in AI?
a) AI deciding who gets a scarce medical resource
b) AI in online shopping
c) AI in social media apps
d) AI for traffic signals
Answer: a) AI deciding who gets a scarce medical resource
13. Beneficence in bioethics means:
a) AI should benefit patients and improve health
b) AI should always be profitable
c) AI should replace human doctors
d) AI should work faster than humans
Answer: a) AI should benefit patients and improve health
14. Which organization has worked on international bioethics guidelines?
a) UNESCO
b) FIFA
c) NASA
d) WHO
Answer: a) UNESCO
15. Confidentiality in bioethics ensures that:
a) Patient data is kept private and secure
b) Patient data is shared on social media
c) Doctors sell patient records
d) AI systems publish medical reports publicly
Answer: a) Patient data is kept private and secure
16. The ethical dilemma of AI replacing doctors comes under:
a) Human dignity and employment concerns
b) Transparency in sports
c) Use of internet speed
d) Entertainment ethics
Answer: a) Human dignity and employment concerns
17. Which of these is an example of violation of bioethics in AI?
a) Using patient data without consent
b) AI helping diagnose cancer early
c) AI monitoring heart rates in hospitals
d) AI assisting disabled people
Answer: a) Using patient data without consent
18. Which bioethical issue arises when AI decides life-support continuation?
a) Autonomy and human dignity
b) Profit-making
c) Entertainment
d) Computer programming errors only
Answer: a) Autonomy and human dignity
19. Bioethics helps ensure that AI in medicine is:
a) Safe, fair, and respects human values
b) Focused only on making profits
c) Used only for rich patients
d) Faster than human doctors at any cost
Answer: a) Safe, fair, and respects human values
20. Why is bioethics important in AI?
a) It ensures AI respects life, dignity, and fairness in healthcare
b) It makes AI more entertaining
c) It reduces the cost of smartphones
d) It improves only internet speed
Answer: a) It ensures AI respects life, dignity, and fairness in healthcare