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Exploring AI Fundamentals

Authored by Ratnanjali Khanna

English

10th Grade

Exploring AI Fundamentals
AI

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10 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is artificial intelligence (AI)?

A method for creating physical robots.

Artificial intelligence (AI) is the simulation of human intelligence in machines.

A type of computer hardware.

A programming language for web development.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Name one application of AI in healthcare.

Telemedicine platforms

Medical image analysis for diagnosis

Patient scheduling software

Electronic health record management

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is machine learning?

Machine learning is a method of data analysis that automates analytical model building.

A hardware component for data storage.

A type of database management system.

A programming language for web development.

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How does AI differ from traditional programming?

AI learns from data and adapts, while traditional programming follows explicit instructions.

AI requires no data to function effectively.

AI is solely based on human-written rules and logic.

Traditional programming can learn and adapt like AI.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Identify a common use of AI in everyday life.

Virtual assistants

Video streaming

Social media browsing

Online shopping

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Explain the concept of supervised learning in machine learning.

Supervised learning is only applicable to image recognition tasks.

Supervised learning requires no data preprocessing before training.

Supervised learning is a machine learning approach where models are trained on labeled data to predict outcomes based on input features.

Unsupervised learning involves training models without any labeled data.

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What role does data play in machine learning?

Data is irrelevant to machine learning models.

Data only serves to store information without any learning.

Data is used solely for data visualization in machine learning.

Data is the foundation for training machine learning models, enabling them to learn patterns and make predictions.

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