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9. Why is accuracy important for data visualisation?
Ans. Accuracy is important for data visualisation because it ensures the use of appropriate graphical representation to
convey right data.
10. What are data visualisation tools?
Ans. Data visualisation tools help us to visually explore and identify relationships between different datasets.
11. What is machine learning?
Ans. Machine learning is a subset of artificial intelligence that uses statistical methods that enable machines to improve with
experiences.
12. Write any one difference between artificial intelligence and machine learning.
Ans. AI aims at making a machine that mimics human intelligence. Whereas, machine learning aims at making a machine
that can learn through data and solve complex problems.
13. Write the names of two data modelling techniques.
Ans. AI models techniques can be broadly classified into two approaches which are rule-based approach and Learning-
based approach.
14. Write the names of any two terms related to decision trees.
Ans. Root Node and Branching.
B. Long answer type questions:
1. Give advantages of using an iterative approach in problem scoping.
Ans. Following are some advantages of using iterative approach in problem scoping:
• Each iteration helps you improve based on the problems identified in the past cycle.
• It is cost effective as the problem is identified and continual testing gives you a clear picture of the status of your
project.
• Testing and debugging are easier with smaller and initial iterations.
2. Explain each element of 4Ws separately.
Ans. ● Who: In this stage, we look for the people who are having the problem. They are the people who are directly affected
by the problem. They are also known as the stakeholders of the problem.
● What: In this, we consider the nature of the problem, and how do we know it’s a problem. To verify any evidence if
it's a real-life problem or just a perception.
● Where: We check for where does the problem arise, the context of the problem.
● Why: It means to understand the root cause of the problem.
3. Differentiate between Training Data and Testing Data.
Ans. Training Data Testing Data
It is data on which we train our AI project model. It is used to check the performance of an AI model.
Example: Marks stored in a system. Example: New marks entered and tested in a system.
4. What are System maps? What information does it provide?
Ans. A system map is a diagrammatic representation of a set of things working together. System map helps us to find
relationships between different elements of the problem which we have scoped. It helps to find a solution to achieve
the goal of our project.
5. What is Application Programming Interface (APIs)?
Ans. APIs are a set of functions and procedures that allow one application to connect to another. So, one of the ways of
collecting data is through APIs that can be used to collect data from social media services for analysis.
6. Why is data exploration an important stage in an AI Project?
Ans. Data exploration cleans the big data to provide an input to an AI project. Terabytes of data sitting in the data centre
unused is a burden, if correctly processed it can become digital gold.
AI Project Cycle 243

