AI Project Cycle – MCQs with Options & Answers

Section A: AI Project Cycle – Overview

Q1. The AI Project Cycle is:
a) A random process
b) A structured process for AI projects
c) A single-step process
d) A manual process only
Answer: b) A structured process for AI projects

Q2. Which of the following is NOT a stage in the AI Project Cycle?
a) Data Acquisition
b) Modelling
c) Cooking Recipes
d) Deployment
Answer: c) Cooking Recipes

Q3. What is the main goal of the AI Project Cycle?
a) To make AI complicated
b) To develop efficient AI solutions
c) To collect unlimited data
d) To replace humans entirely
Answer: b) To develop efficient AI solutions

Q4. In the AI Project Cycle, Modelling refers to:
a) Building mathematical or AI models from data
b) Making art sketches
c) Collecting survey responses
d) Deploying AI in companies
Answer: a) Building mathematical or AI models from data

Q5. Ethics and Morals in AI projects focus on:
a) Increasing profits only
b) Fair and unbiased AI solutions
c) Ignoring users’ rights
d) Reducing computation power
Answer: b) Fair and unbiased AI solutions

Q6. The first stage in an AI Project Cycle is:
a) Data Acquisition
b) Problem Scoping
c) Modelling
d) Deployment
Answer: b) Problem Scoping

Q7. Which stage involves real-world implementation of an AI solution?
a) Data Exploration
b) Deployment
c) Data Collection
d) Cleaning data
Answer: b) Deployment

Q8. In the AI Project Cycle, Evaluation Techniques are used to:
a) Make data messy
b) Check AI model accuracy
c) Avoid testing
d) Skip data cleaning
Answer: b) Check AI model accuracy

Q9. The AI Project Cycle is usually:
a) Linear and never repeated
b) Iterative and repeated as needed
c) Ignored after deployment
d) A one-time step process
Answer: b) Iterative and repeated as needed

Q10. The AI Project Cycle helps in:
a) Developing structured AI solutions
b) Avoiding AI altogether
c) Wasting resources
d) Ignoring ethical use
Answer: a) Developing structured AI solutions

Section B: Problem Scoping and Setting Goals

Q11. Problem Scoping in AI means:
a) Finding a good problem to solve
b) Deploying an AI system directly
c) Writing only code
d) Avoiding goal setting
Answer: a) Finding a good problem to solve

Q12. Which question is important in Problem Scoping?
a) Who will benefit from the solution?
b) What is the budget for lunch?
c) Which phone brand is best?
d) How to decorate the office?
Answer: a) Who will benefit from the solution?

Q13. A clear goal in AI should be:
a) Vague and broad
b) Specific and measurable
c) Difficult to track
d) Irrelevant to stakeholders
Answer: b) Specific and measurable

Q14. Stakeholders in AI problem scoping are:
a) People affected by the solution
b) Only AI engineers
c) Computer hardware suppliers
d) None of these
Answer: a) People affected by the solution

Q15. SMART goals stand for:
a) Short, Medium, Achievable, Right, Tough
b) Specific, Measurable, Achievable, Relevant, Time-bound
c) Simple, Moderate, Accurate, Reliable, Testable
d) Systematic, Modern, Available, Rational, True
Answer: b) Specific, Measurable, Achievable, Relevant, Time-bound

Q16. The scope of a problem defines:
a) Its limits and boundaries
b) The names of engineers
c) The office budget
d) The size of the company logo
Answer: a) Its limits and boundaries

Q17. Setting unrealistic AI goals can lead to:
a) Faster results
b) Project failure
c) Better teamwork
d) Increased accuracy
Answer: b) Project failure

Q18. Identifying constraints in problem scoping means:
a) Knowing the limitations and challenges
b) Ignoring available resources
c) Reducing accuracy
d) Hiring more engineers
Answer: a) Knowing the limitations and challenges

Q19. Which of these is an example of a specific AI goal?
a) “Make delivery faster someday”
b) “Reduce delivery time by 20% within 3 months”
c) “Improve customer service somehow”
d) “Collect unlimited data”
Answer: b) “Reduce delivery time by 20% within 3 months”

Q20. Who should be consulted in problem scoping?
a) Only software developers
b) Only data scientists
c) Stakeholders
d) Office janitors
Answer: c) Stakeholders

Section C: Data Acquisition

Q21. Data Acquisition in AI refers to:
a) Buying computers
b) Collecting data from various sources
c) Hiring AI engineers
d) Building websites
Answer: b) Collecting data from various sources

Q22. Which of the following is NOT a source of data acquisition?
a) Sensors
b) Surveys
c) Games
d) Databases
Answer: c) Games

Q23. Data collected should be:
a) Irrelevant
b) Accurate and reliable
c) Fake and duplicate
d) Random and unstructured
Answer: b) Accurate and reliable

Q24. Which tool is commonly used for online data collection?
a) Google Forms
b) Paint
c) Notepad
d) Calculator
Answer: a) Google Forms

Q25. What is the first step after acquiring data?
a) Directly modelling
b) Cleaning and preprocessing
c) Deploying AI
d) Skipping exploration
Answer: b) Cleaning and preprocessing

Q26. Data Acquisition must respect:
a) Privacy and security rules
b) Only engineers’ ideas
c) Random collection methods
d) Ignoring stakeholders
Answer: a) Privacy and security rules

Q27. Large datasets are often stored in:
a) Textbooks
b) Databases or cloud storage
c) Shopping carts
d) Whiteboards
Answer: b) Databases or cloud storage

Q28. Which type of data is collected by temperature sensors?
a) Audio data
b) Numerical data
c) Video data
d) Image data
Answer: b) Numerical data

Q29. Incorrect or incomplete data is called:
a) Noisy data
b) Gold data
c) Structured data
d) Smart data
Answer: a) Noisy data

Q30. Which of these is an example of public data source?
a) Government portals
b) Secret diaries
c) Personal emails
d) Family albums
Answer: a) Government portals

Section D: Data Exploration

Q31. Data Exploration is also known as:
a) Data Cleaning
b) Data Understanding
c) Model Deployment
d) Problem Scoping
Answer: b) Data Understanding

Q32. Which tool is commonly used for exploring data visually?
a) Tableau
b) Photoshop
c) MS Word
d) Paint
Answer: a) Tableau

Q33. Data Exploration helps to identify:
a) Trends and patterns
b) New machines to buy
c) Office furniture
d) New employees
Answer: a) Trends and patterns

Q34. Outliers in data are:
a) Normal values
b) Extreme values different from others
c) Missing values
d) Clean values
Answer: b) Extreme values different from others

Q35. Missing values in a dataset are usually handled by:
a) Ignoring them
b) Filling them with appropriate methods
c) Copy-pasting random numbers
d) Deleting all data
Answer: b) Filling them with appropriate methods

Q36. Which graph is useful to compare categories of data?
a) Bar chart
b) Paragraph chart
c) Calendar
d) Map
Answer: a) Bar chart

Q37. Correlation in data exploration shows:
a) Relationship between variables
b) Employee salaries
c) Number of holidays
d) Random values
Answer: a) Relationship between variables

Q38. Data exploration helps to decide:
a) Which model to use
b) What food to eat
c) How to paint graphs
d) Which friends to invite
Answer: a) Which model to use

Q39. A scatter plot is best for:
a) Showing relationships between two variables
b) Writing stories
c) Counting students
d) Tracking attendance
Answer: a) Showing relationships between two variables

Q40. What is the benefit of data visualization in exploration?
a) Easier understanding of patterns
b) More confusion
c) Avoiding analysis
d) Creating longer reports
Answer: a) Easier understanding of patterns

Section E: Modelling

Q41. Modelling in AI means:
a) Creating a predictive model from data
b) Collecting data only
c) Making reports manually
d) Deleting datasets
Answer: a) Creating a predictive model from data

Q42. Supervised learning uses:
a) Labelled data
b) Unlabelled data
c) Random numbers
d) No data at all
Answer: a) Labelled data

Q43. Unsupervised learning is based on:
a) Clustering and grouping
b) Labelled training
c) Copying answers
d) Ignoring data
Answer: a) Clustering and grouping

Q44. Which algorithm is used for classification?
a) Decision Tree
b) Sorting
c) Addition
d) Subtraction
Answer: a) Decision Tree

Q45. Training data is used to:
a) Build the model
b) Test the model only
c) Deploy the model
d) Avoid the model
Answer: a) Build the model

Q46. Testing data is used to:
a) Evaluate model performance
b) Build the model
c) Collect features
d) Avoid errors
Answer: a) Evaluate model performance

Q47. Overfitting occurs when:
a) Model memorizes training data but fails on new data
b) Model works on all data
c) Data is incomplete
d) Model never trains
Answer: a) Model memorizes training data but fails on new data

Q48. Which method is used to split data into training and testing sets?
a) Train-test split
b) Cut-paste
c) Divide by 2
d) Random removal
Answer: a) Train-test split

Q49. Accuracy, precision, and recall are used in:
a) Model evaluation
b) Problem scoping
c) Data collection
d) Office management
Answer: a) Model evaluation

Q50. A regression model predicts:
a) Continuous values
b) Only categories
c) Text
d) Shapes
Answer: a) Continuous values

Section F: AI Project Evaluation & Deployment

Q51. The purpose of AI model evaluation is:
a) To measure performance
b) To decorate graphs
c) To hire engineers
d) To avoid AI
Answer: a) To measure performance

Q52. Which of the following is an evaluation metric?
a) Precision
b) Painting
c) Shopping
d) Dancing
Answer: a) Precision

Q53. Cross-validation helps in:
a) Checking model reliability
b) Cleaning data
c) Deploying AI
d) Problem scoping
Answer: a) Checking model reliability

Q54. Deployment in AI means:
a) Using AI solution in real-world applications
b) Testing in lab only
c) Collecting more data
d) Ignoring evaluation
Answer: a) Using AI solution in real-world applications

Q55. Which is an example of AI deployment?
a) Chatbot on a website
b) Random coding
c) Collecting books
d) Painting walls
Answer: a) Chatbot on a website

Q56. Continuous monitoring after deployment ensures:
a) The model remains effective
b) No changes required
c) Errors remain hidden
d) Stakeholders ignored
Answer: a) The model remains effective

Q57. Which of these is NOT part of deployment?
a) Model installation
b) Real-time monitoring
c) User training
d) Cooking food
Answer: d) Cooking food

Q58. Evaluation techniques help in:
a) Improving accuracy of AI
b) Buying new laptops
c) Printing reports
d) Collecting office data
Answer: a) Improving accuracy of AI

Q59. Deployment bridges the gap between:
a) Model development and real use
b) Teachers and students
c) Office and home
d) Books and pens
Answer: a) Model development and real use

Q60. Feedback from deployment helps to:
a) Improve future AI models
b) Close the project
c) Ignore stakeholders
d) Reduce training
Answer: a) Improve future AI models