AI_ML-mcq-set1 – ExamSprint

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Test your basic knowledge in Artificial Intelligence and Machine Learning Concept.

1.

What does KNN stand for?

Key Network Node

Kernel Neural Node

Known Neural Network

K-Nearest Neighbors

None

2.

Unsupervised learning uses:

Unlabeled data

Labeled data

Structured data only

Output data

None

3.

Which distance is commonly used in KNN?

Logical distance

Binary distance

Euclidean distance

Random distance

None

4.

Which of the following is an application of AI?

Banking

Robotics

All of the above

Healthcare

None

5.

Narrow AI is also called:

Super AI

General AI

Weak AI

Strong AI

None

6.

What is Computer Vision?

Audio processing

Text analysis

Image and video understanding

None

7.

A limitation of AI is:

Lack of emotions

Storage

Accuracy

Speed

None

8.

Machine Learning is a subset of:

Networking

Artificial Intelligence

Data Science

Programming

None

9.

Bias in AI leads to:

More storage

Unfair outcomes

Faster processing

Better accuracy

None

10.

What is the future of AI?

Declining

Rapid growth

Ending

Static

None

11.

Which is an AI-based system?

Siri

Alexa

ChatGPT

All of the above

None

12.

What is a Decision Tree?

A neural network

A linear model

A tree-based model for decision making

A clustering algorithm

None

13.

Decision Tree is used for:

Regression only

Both classification and regression

Classification only

Clustering

None

14.

Dataset is:

Collection of data

Code

Single value

Output

None

15.

Machine Learning allows systems to:

Store data

Delete files

Learn from data

Write code

None

16.

What is the regression line?

Tree structure

Curved line

Best fit line

Random line

None

17.

K-Means algorithm is used for:

Sorting

Regression

Classification

Clustering

None

18.

Underfitting occurs when:

Model is perfect

Model is trained well

Model is complex

Model is too simple

None

19.

A feature is:

Input variable

Code

Output

Label

None

20.

Overfitting means:

Model performs well on new data

Model fits training data too well

Model is simple

Model fails completely

None

Time’s up

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