Page 173 - Ai Book - 10

D. Very short answer type question.
1. What is the stage in the AI project life cycle where a model is tested to calculate its efficiency and
performance?

Ans. Evaluation
2. In the context of AI, what is the term for a computing system made up of simple, highly interconnected
processing elements that process information by their dynamic state response to external inputs?
Ans. Neural Network

3. What is the first step in the AI Project Cycle?
Ans. Problem Scoping involves defining the scope of a problem in the AI project life cycle.
4. What is the purpose of Data Acquisition in AI?
Ans. Data Acquisition is the process of identifying and gathering data requirements for an AI project.

5. What is Data Exploration in the AI Project Cycle?
Ans. Data Exploration is the phase of understanding the nature of data in terms of quality and characteristics.
6. What happens in the Modeling phase of AI projects?

Ans. In the Modeling phase, collected data is analyzed based on the gathered project requirements.
7. What is the last step before deployment in the AI project life cycle?
Ans. Evaluation is the last step that determines the efficiency of the model before deployment.

8. What does the 4W’s Problem Canvas include?
Ans. The 4W’s Problem Canvas includes questions about Who, What, Where, and Why related to a problem.
9. What does Unsupervised Learning rely on in AI models?

Ans. Unsupervised Learning works on unlabeled datasets to identify patterns and relationships.
10. What is the primary focus of Reinforcement Learning?
Ans. Reinforcement Learning focuses on training machine learning models to make a sequence of decisions.

11. What does the term “Data Feature” refer in AI projects?
Ans. Data Feature refers to the type of data collected for the scoped problem in AI projects.

E. Short answer type question.
1. What is the purpose of the 4W’s Problem Canvas in the AI Project Cycle?
Ans. The 4W’s Problem Canvas helps identify key elements related to a problem by asking Who, What, Where,
and Why questions.

2. Why is Data Acquisition important in AI projects?
Ans. Data Acquisition is crucial as it involves identifying and gathering all the necessary data requirements for
an AI project.
3. How does Data Exploration contribute to the AI Project Cycle?

Ans. Data Exploration helps understand the nature of data, ensuring its quality and characteristics align with
project goals.

4. What is the significance of the Modelling phase in AI projects?
Ans. The Modelling phase involves analysing collected data based on project requirements and training the
model using machine-learning algorithms.

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