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Introduction_to_AI

Total questions: 30

Worksheet time: 30mins

Name
Class
Date
1.

What does AI primarily aim to do?

a)

Replace hardware components

b)

Develop antivirus software

c)

Create systems that think and act like humans

d)

Increase monitor resolution

2.

Who is considered the father of AI?

a)

Alan Turing

b)

Charles Babbage

c)

John McCarthy

d)

Marvin Minsky

3.

Which of the following is not a goal of AI?

a)

Reasoning

b)

Learning

c)

Data Replication

d)

Problem-solving

4.

Which of the following is not a type of machine learning?

a)

Supervised Learning

b)

unsupervised learning

c)

Integrated learning

d)

Reinforced learning

5.

What is the main purpose of machine learning?

a)

Store data efficiently

b)

Perform calculations faster

c)

Enable machines to learn from data

d)

Increase database size

6.

Which of these is a real-world application of ML?

a)

Face recognition

b)

HTML scripting

c)

Disk partitioning

d)

MS Excel formulas

7.

Rule-based AI systems uses

a)

Training Data

b)

Statistical models

c)

if-then rules

d)

Genetic algorithm

8.

Machine Learning-based AI systems learn from

a)

Predefined rules

b)

Logic Programming

c)

Historical data

d)

Static inputs

9.

In a rule-based AI system, knowledge is stored in

a)

Neural networks

b)

Hidden layers

c)

Knowledge base

d)

Data warehouse

10.
  • A limitation of rule-based AI is

a)

It cannot process text

b)

It requires constant manual updates

c)

it uses big data

d)

it works only in real time

11.

Narrow AI is also known as

a)

Strong AI

b)

Weak AI

c)

Super AI

d)

Deep AI

12.

Which ML type uses labeled data?

a)

Unsupervised

b)

Reinforcement

c)

Supervised

d)

Semi-supervised

13.

Which ML method is like trial and error?

a)

Unsupervised

b)

Reinforcement

c)

Supervised

d)

Deductive learning

14.

Which of these problems is best suited for supervised learning?

a)

Grouping customers

b)

Predicting house prices

c)

Identifying anomalies

d)

clustering data

15.

Which component does reinforcement learning use?

a)

Loss function

b)

Decision tree

c)

Reward signal

d)

Linear regression

16.

Regression is used to

a)

Classify categories

b)

Predict continuous values

c)

Segment data

d)

Encrypt data

17.

Which of the following is a regression algorithm?

a)

Decision Tree

b)

Logistic Regression

c)

Linear Regression

d)

Naive Bayes

18.

In classification, the output is typically

a)

A number

b)

A category

c)

A Data structure

d)

A vector

19.

Clustering is used in

a)

Regression problems

b)

Classification

c)

Unsupervised learning

d)

Supervised learning

20.

In clustering, the goal is to

a)

Minimize reward

b)

Maximize classification

c)

Group similar data

d)

Predict future values

21.

Clustering is helpful in

a)

Labeling spam emails

b)

Image segmentation

c)

Predicting stock prices

d)

Text summarization

22.

Association mining is used to find

a)

Cluster

b)

Classifications

c)

Relationships between variables

d)

Decision Rules

23.

Association rules are often used in

a)

Market Basket Analysis

b)

Clustering

c)

Regression

d)

Classification

24.

Which type of ML model learns from feedback?

a)

Unsupervised

b)

Reinforcement

c)

Supervised

d)

Regression

25.

Weak AI is best defined as

a)

Systems with full human-like consciousness

b)

Systems mimicking intelligent behavior in narrow tasks

c)

Systems with emotions

d)

Systems that can generalize across domains

26.

Expert systems are described as

a)

Autonomous robots

b)

Systems embedding human expert knowledge

c)

Neural networks

d)

Unsupervised learning tools

27.

K‑Means clustering is used to

a)

Fit regression lines

b)

Partition data into groups

c)

predict labels

d)

Generate reward signals

28.

Decision trees can be used for

a)

Only regression

b)

Only classification

c)

Both regression and classification

d)

clustering

29.

Which algorithm is suited for market-basket analysis?

a)

Logistic regression

b)

K-Means

c)

Apriori

d)

Decision Tree

30.

Unsupervised learning is particularly useful when

a)

Labels are abundant

b)

Pre-defined outputs exist

c)

Labels are not given and patterns must be found

d)

Reward feedback is present