AI Project Cycle - Questions & Answers | Class 10 - Artificial Intelligence | Part -1 #eduvictors #Class10AI

 AI Project Cycle - Questions & Answers | Class 10 - Artificial Intelligence | Part -1

Q1. What is a Project Cycle?

Answer: A Project Cycle is a step-by-step process to solve problems using proven scientific methods and drawing inferences about them.

Q2. What is the AI Project Cycle?

Answer: The AI project cycle provides us with a framework for planning, organising, executing and implementing an AI project to achieve a target. The process of developing AI machines has different stages that are collectively known as the AI project cycle.

Q3. Name all the stages of an AI Project cycle.

Answer: Problem Scoping, Data Acquisition, Data Exploration, Modeling and Evaluation.

OR

Problem Scoping - Understanding the problem

Data Acquisition - Collecting accurate and reliable data

Data Exploration - Arranging the data uniformly

Modelling - Creating Models from the data

Evaluation - Evaluating the project

Q4. What is problem scoping in the AI project cycle?

Answer: Problem Scoping refers to understanding a problem, finding out various factors which affect the problem, define the goal or aim of the project.

Q5. List the 4Ws in problem scoping?

Answer: 4Ws— Who, What, Where and Why.

Q6(MCQ). Which of the following refers to “Where” among “4W” Canvas?

(a) Stakeholders

(b) Nature of the problem

(c) Context/situation/location

(d) Solution or benefits to the stakeholders

Answer: (c) Context/situation/location

In the "4W" Canvas, "Where" refers to the context, situation, or location.

Q7. What is a problem statement template and why is it important?

Answer: A problem statement template allows us to summarise all the key points in a single format. This way, whenever we need to revisit the foundation of the problem, we can refer to the template and clearly understand its essential elements.

Q8. Define Data Acquisition.

Answer: It is the second stage of the AI project life cycle. Data acquisition/data gathering refers to collecting/gathering all data required for an AI project.

Q9. What precautions should be taken when acquiring data for developing an AI project?

Answer: Data should be gathered from reliable sources and must be accurate. Redundant and irrelevant data should be excluded from the predictions.

Q9. What is data?

Answer: Data can be a piece of information or facts and statistics collected together for reference or analysis.

Q10. What is training data?

Answer: It is the data on which we train our AI project model. It should be authentic and relevant.

Q11. What is testing data?

Answer: It is used to check the performance of An AI model.

Q12. What is the Data feature?

Answer: Data features refer to the type of data you want to collect for the problem scoped.

Q13. What is Big Data?

Answer: It includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.

Q14. What are the various ways to collect data?

Answer: Surveys, Web Scraping, Cameras, Observations, API (Application Program Interface)

Q15. Explain the Data Exploration stage.

Answer: This is the third stage in the AI project cycle. It refers to exploring the large data to uncover the patterns or trends needed for the AI project. It is considered to be the first step in

data analysis where unstructured data is explored, researched, filtered and visualised to decide the strategy for the type of model used in the later stage.

Q16. What are the two sub-stages of data exploration?

Answer: 

i.  Data Cleaning

ii. Data Visualization

Q17. What is Data Cleaning? List any three commonly found errors found in data.

Answer: Data cleaning helps eliminate commonly found errors and mistakes in a data set. Following are the 3 commonly found errors in data.

i.   Outliers: Data points existing out of the range.

ii.  Missing data: Data points missing at certain places.

iii. Erroneous data: Incorrect data points.

Q18. Below is a dataset showing the number of hours spent on homework and the corresponding test scores for a group of students.

Student Name	Homework Hours	Test Score(%)
Alok	2	85
Bharati	4	90
Chandani	3	82
Dhaam	0	60
Eisha	5	94
Falak	-2	88
Gauri	8	40
Hina	2	92
Ishan		78
Jai	3	83

Carefully review the dataset and answer the following questions by identifying outliers, missing data, and erroneous data.

Questions:

i: Identify the student(s) with missing data in the dataset.

ii: Are there any values in the dataset that seem clearly incorrect or impossible (erroneous)? If so, name the student(s) and explain why.

iii. An outlier is a data point significantly different from the others. Based on the "Homework Hours" and "Test Score" columns, which student(s) might be considered outliers? Justify your answer.

Answers:

i. Missing Data:

Ishan: The "Homework Hours" for Isaac is missing.

ii. Erroneous Data:

Falak: Homework hours cannot be negative (-2 is incorrect).

Outliers:

iii. Outlier

Gauri: Gauri worked for 8 hours but received a test score of 40%, which seems significantly lower than expected based on other students' results.

Q19. What is data visualisation?

Answer: It refers to a process of representing data in a pictorial/graphical format using various visualisation tools that help us understand various trends, relationships and patterns.

Q20 (MCQ): Anusuya went to her hometown, Lucknow, where not all the facilities available in urban areas are present. She is trying to choose a problem to solve. In the project cycle, which step helps in selecting a problem to solve?

(a) Problem scoping

(b) Evaluation

(c) Data acquisition

(d) Modelling

Answer: (d) Modelling

Q21. Why is there a need to explore data through visualization?

Answer:

i.   We want to quickly understand the trends, relationships, and patterns within

the data.

ii.  It helps us define a strategy for which model to use at a later stage.

iii. Visual representation is easier to understand and communicate to others.

> See Modelling technique related Q & A here (TBD).

👉See Also:

Introduction to AI - Questions and Answers
Intelligence and Its Types
Artificial Intelligence Acronyms
Entrepreneurial Skills - II (Questions and Answers)