Training data is used to evaluate the performance of an AI model.
Answer
False
Reason — Training data is the initial dataset used to train an AI model. Testing data is used to evaluate the performance of the AI model.
AI is a subset of machine learning and deep learning.
Answer
False
Reason — Artificial Intelligence is the broader field. Machine learning and deep learning are subcategories of Artificial Intelligence.
A problem statement template helps in clearly defining the problem to be solved.
Answer
True
Reason — The Problem Statement Template aids in summarising all the key points in the 4Ws problem canvas into a single template, which enables us to clearly understand and remember the important aspects of the problem to be solved.
Data Exploration is performed before Data Acquisition in the AI project cycle.
Answer
False
Reason — In the AI project cycle, data acquisition is performed first to collect the relevant data, and data exploration is done after acquiring the data to analyse patterns, trends, and relationships.
Testing data is used to evaluate the performance of the AI model.
Answer
True
Reason — Testing data is used to evaluate the performance of the AI model. It is the data that the AI algorithm has not seen before and helps to check the accuracy of the AI model.
Tableau is a popular data visualisation tool.
Answer
True
Reason — Tableau is a popular data visualisation tool. It is a user-friendly and interactive tool that supports large datasets and provides options to collaborate with others and securely share data from a variety of sources.
Data can be in the form of text, video, images, audio, etc.
Answer
True
Reason — Data can be in the form of text, video, images, audio, and so on.
In evaluation stage, the model is tested using testing data.
Answer
True
Reason — In the evaluation stage, the AI model is tested using testing data and the output is compared with the expected outcome to assess its accuracy and reliability.
In a system map, a positive (+) sign means that both elements are directly related to each other.
Answer
True
Reason — In a system map, if the arrow goes from one element to another with a positive (+) sign, it means that both elements are directly related to each other. If one element increases, the other also increases, and vice versa.
The deep learning models understand how the human brain works in different situations and then try to recreate its behaviour.
Answer
True
Reason — Deep learning models understand how the human brain works in different situations and then try to recreate that behaviour. For example, a deep learning model in a driverless car can identify a person crossing the road.
Problem scoping mainly focuses on ............... .
- Finding the main cause of the problem
- Identifying a problem and defining its goal
- Coming up with the solution for the problem
- Implementing the solution to the problem
Answer
Identifying a problem and defining its goal
Reason — Problem scoping is the stage where we set clear goals and outline the objectives of the AI project by understanding and defining the problem to be solved.
Which of the following is not a stage of the AI project cycle?
- Initiating
- Data acquisition
- Modelling
- Evaluation
Answer
Initiating
Reason — The AI project cycle consists of five stages: problem scoping, data acquisition, data exploration, modelling, and evaluation. “Initiating” is not included as a stage of the AI project cycle.
Which of the following are the parameters of the 4Ws Canvas?
- Which? What? Who? When?
- What? When? Where? Why?
- Who? What? Where? Why?
- None of these
Answer
Who? What? Where? Why?
Reason — The 4Ws Problem Canvas is a structured framework that helps in problem scoping and consists of four parameters: Who? What? Where? Why?
Which of the following is one of the most reliable and authentic sources of data?
- Social media posts
- Open-sourced government portals
- Private company’s data
- None of these
Answer
Open-sourced government portals
Reason — One of the most reliable and authentic sources of information is the open-sourced websites hosted by the government, which provide data in a suitable format that can be downloaded and used.
Which of the following are suitable sources for data acquisition?
- Surveys
- Web scraping
- Sensors and cameras
- All of these
Answer
All of these
Reason — Surveys, web scraping, and sensors and cameras are all listed as suitable sources for data acquisition in an AI project.
What are the important factors to consider when collecting data for an AI project?
- Data should be authentic, reliable, and accurate
- Data should be obtained from random websites on the internet
- Data collection methods can be unethical and unlawful
- Private data can be accessed without permission
Answer
Data should be authentic, reliable, and accurate
Reason — Data forms the basis of the AI project, hence, the data has to be authentic, reliable, and accurate. The methods of obtaining data should also be ethical and lawful, and accessing private data without permission is an offence.
Which data visualisation tool is a popular Python library that allows for creating static, animated, and interactive visualisations?
- Tableau
- Highcharts
- Excel
- Matplotlib
Answer
Matplotlib
Reason — Matplotlib is a Python library used for creating static, animated, and interactive visualisations in Python.
How does data visualisation help in data exploration?
- Simplifies complex data for easier comprehension
- Uncovers hidden relationships and anomalies in data
- Provides insights into trends, relationships, and patterns in data
- All of these
Answer
All of these
Reason — Data visualisation simplifies complex data, helps uncover hidden relationships or anomalies, and provides insights into trends, relationships, and patterns present within the data.
Which type of chart is used to represent the proportion or percentage of different categories as a whole?
- Bar chart
- Line chart
- Pie chart
- Area chart
Answer
Pie chart
Reason — A pie chart represents data in a circular form divided into sectors, where the size of each sector is proportional to the quantity it represents, and is used to show the proportion or percentage distribution of different categories within a whole.
Which of the following is a subcategory of AI that enables machines to learn on their own and improve with time through experience?
- Mathematics
- Machine learning
- Deep learning
- Rule-based approach
Answer
Machine learning
Reason — Machine learning enables machines to learn on their own and improve with time through experience by learning from data and improving their performance in subsequent iterations.
Which of the following is based on neural networks with multiple layers that mimic the working of the human brain?
- Deep learning
- Machine learning
- Rule-based learning
- None of these
Answer
Deep learning
Reason — Deep learning is based on neural networks with multiple layers that mimic the working of the human brain.
Which of the following stages of the AI project cycle focuses on testing the selected AI model, and the results need to be compared with the expected outcome?
- Evaluation
- Data exploration
- Data acquisition
- Problem scoping
Answer
Evaluation
Reason — The evaluation stage focuses on testing the selected AI model using testing data and comparing the results with the expected outcome to assess the accuracy and reliability of the model.
In which approach does the machine follow the rules defined by the developer and produce the required output?
- Learning-based approach
- Rule-based approach
- Both a and b
- None of these
Answer
Rule-based approach
Reason — In the rule-based approach, the developer feeds data along with rules to the model, and the machine follows the rules defined by the developer to produce the required output.
Which key component of AI evaluation is used for making forecasts about unknown data using a trained model?
- Reality
- System map
- Prediction
- Problem statement template
Answer
Prediction
Reason — Prediction is the process of making forecasts or inferences about unknown or future data using a trained AI model and the patterns it has learned from training data.
............... is a visual diagram that shows how all the elements are connected or related to each other.
- 4Ws canvas
- System map
- Decision tree
- Pixel It
Answer
System map
Reason — System maps are visual diagrams that help to see and understand the different parts or elements of the AI project and show how all the elements are connected or related to each other.
Fill in the blanks:
- ................ is the process of assessing the reliability and efficiency of any AI model.
- The 4Ws Problem Canvas provides structured framework in the ................ stage of the AI project cycle.
- Once a model has been developed and trained, it is important to thoroughly test its ................ and calculate its efficiency.
- AI ................ refers to developing a program or algorithm that can be used to draw conclusions or generate predictions.
- A/An ................ chart is a graph that shows the relationship between two set of values.
- ................ data is the initial dataset used to train an AI model.
- Data features describe the type of information that will be collected in response to the ................ statement.
- The ................ helps to find the relationship between the elements of the scoped problem.
- In the 4Ws canvas, the ................ block helps identify the people who are directly or indirectly affected by the problem.
- In ................ learning, the model is trained on a labelled dataset.
Answer
- Evaluation is the process of assessing the reliability and efficiency of any AI model.
- The 4Ws Problem Canvas provides structured framework in the Problem Scoping stage of the AI project cycle.
- Once a model has been developed and trained, it is important to thoroughly test its Performance and calculate its efficiency.
- AI Modelling refers to developing a program or algorithm that can be used to draw conclusions or generate predictions.
- A/An Scatter chart is a graph that shows the relationship between two set of values.
- Training data is the initial dataset used to train an AI model.
- Data features describe the type of information that will be collected in response to the Problem statement.
- The System map helps to find the relationship between the elements of the scoped problem.
- In the 4Ws canvas, the Who block helps identify the people who are directly or indirectly affected by the problem.
- In Supervised learning, the model is trained on a labelled dataset.
What is meant by AI project deployment?
Answer
AI project deployment is the process of implementing an AI model in a real-world scenario. In this stage, the AI model is integrated into the desired software or system and packaged in such a way that it can be used for practical applications. The main goal of AI project deployment is to make the AI model useful for solving real-world problems.
Define Regression. Give a real-life example of regression.
Answer
Regression is a type of supervised learning model used to predict continuous numerical values. Continuous data means data that can have any value within a certain range, for example, the price of a product or the salary of an employee. It is used to predict the behaviour of one variable depending on the value of another variable.
Example: A real-life example of regression is predicting the temperature for the next day based on historical weather data, including factors like humidity and cloud cover.
What are system maps?
Answer
System maps are visual diagrams that help to see and understand the different parts or elements of an AI project. They show how all the elements are connected or related to each other and help in understanding the system’s boundaries and how it interacts with elements in its surroundings.
State any five factors which are affecting the problem in AI project.
Answer
Any five factors that affect the problem in an AI project are:
- Stakeholders (Who?) – The people/groups who are directly affected by the problem.
- Nature of the problem (What?) – What exactly is the issue that needs to be solved (clear problem statement).
- Evidence that it is a real problem (What?) – Observations/records/reports showing the problem actually exists.
- Context / Location (Where?) – Where the problem occurs (place, situation, platform, community, etc.).
- Reason/Impact (Why?) – Why it is worth solving and how it will benefit the stakeholders/society (better quality of life, safety, efficiency, etc.).
Give one application each of machine learning and deep learning.
Answer
Application of Machine Learning — Predicting the weather forecast for the next few days based on historical weather data.
Application of Deep Learning — A driverless car identifying a person crossing the road using image recognition.
State the function of sensors.
Answer
Sensors detect changes in the surroundings (environment) such as temperature, pressure, light, sound, distance or motion and convert them into electrical signals or digital data. This data is then processed and analysed by AI systems to take suitable action or decisions.
List the five stages in the AI project cycle. Write brief description of each.
Answer
The AI project cycle consists of five stages. It is a structured framework that outlines the step-by-step process of developing and implementing an AI system. The stages are as follows:
- Problem Scoping: In this stage, we clearly identify the problem, set clear goals, and define what we want the AI system to achieve. It involves understanding the needs, objectives, constraints, and planning how to solve the problem.
- Data Acquisition: Here we collect relevant data needed for the AI system from reliable sources (e.g., surveys, sensors, cameras, observations, APIs, web scraping). The data must be accurate and suitable for the problem.
- Data Exploration: In this stage, we explore and study the data to find patterns, trends, relationships, and possible errors. Data is often represented using charts/graphs to understand it better and decide what information (features) is important.
- Modelling: After understanding the data, we choose and build an AI model (algorithm/program) and train it using data so that it can make predictions or decisions.
- Evaluation: Finally, we test the model’s performance by comparing its results with expected outcomes. If the accuracy is good, the model can be used/deployed otherwise, it is improved and tested again.
Describe the two types of data used in the AI project cycle.
Answer
The two types of data used in the AI project cycle are:
Training Data: Training data is the initial dataset used to train an AI model. It is a set of examples that helps the AI model learn and identify patterns or perform particular tasks. The data used for training should be aligned with the problem statement and must be sufficient, relevant, accurate, and wide-ranging so that the model can learn effectively.
Testing Data: Testing data is used to evaluate the performance of the AI model. It is the data that the AI algorithm has not seen before. Testing data helps in checking the accuracy and reliability of the AI model and represents the data that the model will encounter in real-world situations.
List any five data features that will be required to build an AI system to classify images of animals into different species.
Answer
The following are five data features that can be collected to build an AI system to classify images of animals into different species:
- Colour (dominant body colours, colour distribution).
- Body shape and size (long neck, round body, size of head, legs, tail, wings, etc.).
- Texture (fur, feathers, scales, smooth vs rough appearance).
- Facial features (eyes, ears, beak, or snout).
- Patterns/markings (stripes, spots, patches, rings on tail, etc.).
What is the difference between the rule-based approach and learning based approach of AI modelling?
Answer
Differences between rule-based approach and learning based approach of AI modelling:
| Rule-based Approach | Learning-based Approach |
|---|---|
| The machine follows the rules defined by the developer. | The machine learns on its own from the data. |
| AI is achieved through rule-based technique. | AI is achieved through learning technique. |
| It typically uses labelled data. | It can handle both labelled and unlabelled data. |
| It may require less training time. | It requires more training time. |
What is data exploration? Explain pie and scatter chart.
Answer
Data exploration is the process of exploring and analysing the collected data to interpret patterns, trends, and relationships. Since data is usually in large quantities, different data visualisation techniques are used to easily understand the data and gain meaningful insights.
Pie Chart
A pie chart represents data in a circular form divided into sectors, where the size of each sector is proportional to the quantity it represents. It is used to understand the contribution of individual categories to the whole and is suitable for showing proportional or percentage distribution of data.
Scatter Chart
A scatter chart is a plot represented with the help of dots, where each dot represents a single data point. It is used to show the relationship between two sets of values and is useful for identifying patterns, correlations, and relationships between two continuous variables.
Applying the 4Ws problem canvas, identify the stakeholders, nature of the problem, context, and benefits of solving the problem for the following scenario:
Developing a mobile app to help users track their daily water consumption
Answer
Applying the 4Ws Problem Canvas to the given scenario:
Scenario:
Developing a mobile app to help users track their daily water consumption
Who? (Stakeholders)
- Users of the mobile app
- Health-conscious individuals
- Families and households
- Healthcare professionals
What? (Nature of the problem)
- Many people do not track their daily water intake.
- Lack of awareness about adequate water consumption can lead to dehydration and health issues.
Where? (Context or location)
- The problem is experienced in daily life, at homes, workplaces, schools, and other places where people carry out their routine activities.
- The solution will be used on mobile devices.
Why? (Benefits of solving the problem)
- Helps users monitor and improve their daily water intake.
- Promotes better health and hydration habits.
- Increases awareness about the importance of drinking sufficient water.
- Contributes to a healthier lifestyle.
Suppose, you are building an AI application that recommends movies to viewers. Which data features would be important for this AI project?
Answer
For a movie recommendation AI, the data features should describe both the viewer and the movies, so the model can find patterns and recommend accurately.
Important data features for this AI project
1. Viewer features
- Movies/genres the viewer has watched
- Viewer’s ratings/likes/dislikes
- Watch time / completion rate (whether they finished or dropped a movie)
- Search history and movies added to watchlist
- Viewer preferences like language, age group, and preferred content type (action, comedy, family, etc.)
2. Movie (Item) features
- Genre (action, romance, thriller, etc.)
- Language
- Cast and director
- Release year
- Movie duration
- Keywords/tags (e.g., "school life", "sports", "superhero")
- Age rating (U/A, A, etc.)
3. Context features
- Time of viewing (weekend vs weekday, night vs day)
- Device/platform used (mobile/TV)
- What’s trending/popular in the region
These features help the AI learn relationships between viewer choices and movie characteristics, and then recommend movies that match the viewer's taste.