Class 10 AI Chapter - AI Project Cycle Topic - Question/Answer - Arvindzeclass

Class 10 Artificial Intelligent Code 417 Solutions

Session 2025-26

Artificial Intelligence code 417 syllabus pdf class 10 solutions. Class 10 AI Book. Part - A, Part - B, and Python. This article provides complete solution for class 10 AI (Artificial Intelligence) Code - 417 students according to new syllabus 2025 – 26. In this article first of all go through the AI Code - 417 syllabus and then follow chapter wise notes. Class 10 AI MCQs Quiz with answers.

Part A - Employability Skills Question/Answers

Chapter 1 - Communication Skills - II

Chapter 2 - Self - Management Skills - II

Chapter 3 - ICT Skill - II

Chapter 4 - Entrepreneurial Skills - II

Chapter 5 - Green Skills - II

Part B - Subject Specific Skills Question/Answers

Chapter 1 - AI Project Cycle & Ethics

Chapter 2 - Modelling in AI

Chapter 3 - Evaluating Models

Chapter 4 - Statistical Data

Chapter 5 - Computer Vision

Chapter 6 - Natural Language Processing

Chapter 7 - Advance Python

#What is AI Project Cycle?

In AI Project Cycle, these are the steps to be followed to make a cup of coffee. Software is also designed following some steps. There is also set of rules to be followed while making a AI projects. These are the steps to make AI projects:

AI Project Cycle

1) Problem Scoping: This is first step in AI project cycle where problem is defined and it is identified that how AI technology can help to solve the problem. To understand the problem 4W’s technique is used.

i) Who: It identifies who will be affected by this AI solution. It defines the users and stakeholder.

ii) What: It finds out the specific problem which needs to be addressed by AI solution. It defines the outcome of the AI project.

iii) Where: It finds out where the AI project will work. It searches environment and specific domain to be used.

iv) Why: It finds out the reason to solve the problem. It looks for the impact of the solution on business and users.

2) Data Acquisition: In this stage raw data is gathered which needs to be analysed. A data could be anything like text, image, audio, video, email etc. The data is gathered from different sources like newspaper, internet, and journals, academic. Data gives valuable insight for developing the AI model.

3) Data Exploration: This is an important phase in AI project cycle because large amount of data is analysed to find the meaningful patters. There are many visualising tools like MS Excel to get the insight of data in the form of charts, graphs.

4) Modeling: In this phase AI system is designed using machine learning algorithm and the AI system is trained using any learning method. This is the crucial phase in AI project cycle because previous steps were conducted to design accurate AI system.

5) Evaluation: In this phase AI system’s performance is evaluated and ensured that it meets the predefined goal. If AI model doesn’t meet the objective of the project, it means some changes are required. When AI model gives accurate results and aligns with the user’s requirement, it means AI model is ready for deployment phase.

6) Deployment: In this AI system is integrated in working environment under the supervision of experts. It is also monitored that AI system is giving accurate result. Regular AI system’s performance is analysed. It may need some changes due to change in data.

#What is the use of 4Ws in Problem Scoping in AI Project Cycle?

1. WHO – Who is facing the problem?

Meaning: Identifies the people, group, or stakeholders affected by the problem.
Why important? Because AI projects are made for people’s needs. If we don’t know who the user is, the solution may not be useful.
Questions to ask:
Who are the end-users?
Who will benefit from the solution?
Who is currently struggling because of the problem?
Example:
Suppose the problem is traffic congestion in a city.
Who: Daily commuters, traffic police, public transport users.
4Ws

2. WHAT – What exactly is the problem?

Meaning: Defines the nature of the problem clearly.
Why important? If the problem statement is vague, the AI solution won’t be accurate.
Questions to ask:
- What is happening that shouldn’t be?
- What difficulties are being faced?
- What is the expected outcome?
Example (traffic congestion):
What: Heavy traffic during peak hours, leading to delays and fuel wastage.

3. WHERE – Where is the problem happening?

Meaning: Locates the place, situation, or environment where the problem exists.
Why important? AI models need specific data, and the context (location/conditions) helps refine the solution.
Questions to ask:
- Where does the problem occur most frequently?
- Is it in a specific area, department, or environment?
Example (traffic congestion):
Where: On main city roads, at traffic signals, and during office hours.

4. WHY – Why is it important to solve the problem?

Meaning: Explains the purpose behind solving the problem.
Why important? If there is no clear benefit, the project may waste time and resources.
Questions to ask:
- Why should we put effort into solving this problem?
- Why will AI help better than traditional solutions?
Example (traffic congestion):
Why: To save people’s time, reduce pollution, improve city transportation efficiency.

✨ Final Summary

Who → identifies the stakeholders.
What → clarifies the problem.
Where → locates the problem.
Why → explains the importance of solving it.

👉 These 4Ws help in writing a well-scoped problem statement, which is the foundation of a successful AI project.

#What is Domains of AI? Explain Statistical Data, Computer Vision, and NLP.

Domains of AI

Meaning: Domains of AI are the different areas or branches where Artificial Intelligence is applied.
Each domain focuses on a specific capability of machines—like learning from data, understanding language, or seeing the world.
Example domains: Machine Learning, NLP, Computer Vision, Robotics, Expert Systems, etc.
AI Domains

1. Statistical Data (Machine Learning & Data Analysis Domain)

Meaning: AI uses statistics and data to recognize patterns, make predictions, and take decisions.
How it works:
- Collect large amounts of data
- Use statistical techniques (probability, averages, regression, etc.)
- Train AI models to find trends and make predictions
Applications:
- Predicting weather using past data
- Stock market prediction
- E-commerce product recommendations (Amazon, Flipkart)
Example: Netflix suggests movies to you based on the statistical analysis of what people with similar tastes watched.

2. Computer Vision

Meaning: AI’s ability to see, analyze, and understand images or videos, just like human eyes and brain.
How it works:
- A camera collects visual data (pictures or video)
- Algorithms process it to detect shapes, objects, or patterns
- AI interprets it and takes action
Applications:
- Face recognition in smartphones
- Self-driving cars detecting pedestrians and signals
- Healthcare: scanning X-rays or MRIs for diseases
Example: When Facebook auto-tags you in a photo, that’s computer vision in action.

3. Natural Language Processing (NLP)

Meaning: AI’s ability to understand, interpret, and generate human language (both text and speech).
How it works:
- Breaks sentences into words
- Understands grammar, meaning, and context
- Generates a meaningful response
Applications:
- Chatbots
- Google Translate
- Voice assistants (Siri, Alexa, Google Assistant)
Example: When you say, “Hey Google, play music,” NLP helps Google understand your command and act on it.

✨ Final Summary

Domains of AI = Areas where AI is applied.
Statistical Data → AI uses numbers, data, and patterns to predict outcomes.
Computer Vision → AI “sees” and understands images/videos.
NLP → AI understands and communicates in human language.

#What are the five Applications of Statistical Data in AI Domains?

The applications of Statistical Data in AI domains.
Since statistical data is the backbone of AI (because AI learns from data), it is applied in many areas. Here are five important ones:

Five Applications of Statistical Data in AI Domains

1. Predictive Analytics

Using past data to predict future outcomes.
Example: Weather forecasting, sales prediction, predicting exam scores of students.

2. Recommendation Systems

Suggesting items to users based on data patterns and preferences.
Example: Netflix recommending shows, Amazon suggesting products, YouTube video suggestions.

Application of Statistical Data

3. Fraud Detection

Identifying unusual or suspicious activities by analyzing data trends.
Example: Detecting credit card fraud by spotting transactions that don’t match normal spending behavior.

4. Healthcare and Medical Diagnosis

Analyzing patient records and medical test results to detect diseases.
Example: Predicting chances of diabetes, heart attack, or cancer using patient’s historical data.

5. Customer Behavior Analysis

Studying how customers behave by analyzing purchase history, browsing patterns, and feedback.
Example: E-commerce companies using data to target ads or offer personalized discounts.

✨ Summary

Applications of Statistical Data in AI Domains include:

Predictive Analytics
Recommendation Systems
Fraud Detection
Healthcare & Medical Diagnosis
Customer Behavior Analysis

#What are the five Applications of Computer Vision in AI Domains?

Five Applications of Computer Vision in AI Domains

1. Facial Recognition

AI can identify and verify people’s faces from images or videos.
Examples: Unlocking phones with Face ID, airport security checks, attendance systems.

2. Autonomous Vehicles (Self-driving Cars)

Computer vision helps vehicles detect lanes, traffic lights, pedestrians, and obstacles.
Examples: Tesla Autopilot, Google Waymo cars.

3. Healthcare (Medical Imaging)

AI analyzes medical images (X-rays, MRIs, CT scans) to detect diseases early.
Examples: Detecting tumors, fractures, or lung infections from scans.
Application of CV
Application of CV

4. Surveillance and Security

AI-powered CCTV systems can monitor activities, recognize suspicious behavior, and detect intrusions.
Examples: Smart city surveillance cameras, crime prevention systems.

5. Retail and E-commerce

Computer vision helps in product identification, inventory management, and virtual try-ons.
Examples: Amazon Go (cashier-less stores), online apps that let you “try” clothes or glasses virtually.