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AI & ML

Total questions: 79

Worksheet time: 47mins

Name
Class
Date
1.

What is Machine learning?

a)

The autonomous acquisition of knowledge through the use of computer programs

b)

he autonomous acquisition of knowledge through the use of manual programs

c)

The selective acquisition of knowledge through the use of computer programs

d)

The selective acquisition of knowledge through the use of manual programs

2.

__________________ algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.

a)

Artificial Intelligence

b)

Machine Learning

c)

Deep Learning

d)

Traditional Learning

3.

What device below is not an example of Machine Learning?

a)

Wearable fitness tracker

b)

Google Assistant

c)

Speech to Text

d)

Google Search

e)

None of the above

4.

_______________________ is a category of an algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.

a)

Artificial Intelligence

b)

Machine Learning

c)

Deep Learning

d)

Traditional Learning

5.

In this type of Machine Learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training. The output is dependent upon the coded algorithms.

a)

Supervised Learning

b)

Unsupervised Learning

c)

Reinforcement Learning

d)

Technique Learning

6.

from the picture, what kind of programming is it?

a)

Traditional Programming

b)

Machine Learning

c)

Modern Programming

d)

Traditional Learning

7.

What kind of learning algorithm for "Future stock prices or currency exchange rates"?

a)

Recognizing Anomalies

b)

Prediction

c)

Generating Patterns

d)

Recognition Patterns

8.

Which of the following is not type of learning?

a)

Semi-unsupervised Learning

b)

Unsupervised Learning

c)

Supervised Learning

d)

Reinforcement Learning

9.

Real-Time decisions, Game AI, Learning Tasks, Skill Aquisition, and Robot Navigation are applications in ...

a)

Unsupervised Learning: Clustering

b)

Supervised Learning: Classification

c)

Reinforcement Learning

d)

Unsupervised Learning: Regression

10.

Fraud Detection, Image Classification, Diagnostic, and Customer Retention are applications in ...

a)

Unsupervised Learning: Clustering

b)

Supervised Learning: Classification

c)

Reinforcement Learning

d)

Unsupervised Learning: Regression

11.

This picture shows a result of ...

a)

Supervised Learning: Classification

b)

Unsupervised Learning: Regression

c)

Unsupervised Learning: Prediction

d)

Supervised Learning: Regression

12.

__________________ algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.

a)

Artificial Intelligence

b)

Machine Learning

c)

Deep Learning

d)

Traditional Learning

13.

What device below is not an example of Machine Learning?

a)

Wearable fitness tracker

b)

Google Assistant

c)

Speech to Text

d)

Google Search

e)

None of the above

14.

In this type of Machine Learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training. The output is dependent upon the coded algorithms.

a)

Supervised Learning

b)

Unsupervised Learning

c)

Reinforcement Learning

d)

Technique Learning

15.

What type of machine learning algorithm makes predictions when you have a set of input data and you know the possible responses?

a)

Unsupervised

b)

Reinforcement

c)

Supervised

d)

Deep Learning

16.

What kind of learning algorithm for "Facial identities or facial expressions"?

a)

Recognizing Anomalies

b)

Prediction

c)

Generating Patterns

d)

Recognition Patterns

17.

ML is a field of AI consisting of learning algorithms that?

a)

Improve their performance

b)

At executing some task

c)

Over time with experience

d)

All of the above

18.

Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting?

a)

Classifying emails as spam or not spam

b)

Watching you label emails as spam or not spam

c)

The number of emails correctly classified as spam/not spam

d)

None of the above

19.

Labeled Data are used in _______ Machine Learning algorithm

a)

Supervised

b)

Unsupervised

20.

Unlabeled Data are used in _______ Machine Learning algorithm

a)

Supervised

b)

Unsupervised

21.

Google Translate uses ________________ to improve its results.

a)

Machine Learning

b)

Internet

c)

Machine Optimization

d)

Data Warehouses

22.

Artificial Intelligence is superset of ________________________ & ________________________ ,

a)

Machine Learning & Neural Networks

b)

Machine Learning & Deep Learning

c)

Deep Learning & Neural Networks

23.

AI is a field of computer science aimed at developing machines which are intelligent enough to do certain tasks that would normally be performed only by (a)  

24.

___________________ is set of algorithms that allows computers to learn from data without being explicitly programmed.

a)

Machine Learning

b)

Deep Learning

c)

Neural Networks

25.

This requires computer scientists to formulate general-purpose learning algorithms that help machines learn more than just one task.

a)

Machine Learning

b)

Deep Learning

c)

Neural Networks

26.

Deep learning is often made possible by artificial neural networks, which imitate ______________, or_____________________.

a)

AI & ML

b)

Neurons or Brain Cells

27.

The neural net models use ___________ and _________________ principles to mimic the processes of the human brain, allowing for more general learning.

a)

Maths & Science

b)

Maths & Computer Science

c)

Human Brain & Science

28.

What kind of learning algorithm is used for detecting anomalies in data?

a)

Supervised Learning

b)

Unsupervised Learning

c)

Reinforcement Learning

d)

Deep Learning

29.

Which type of machine learning algorithm is best suited for predicting stock prices?

a)

Supervised Learning

b)

Unsupervised Learning

c)

Reinforcement Learning

d)

Deep Learning

30.

What is the main difference between supervised and unsupervised learning?

a)

Supervised learning requires labeled data, while unsupervised learning does not

b)

Unsupervised learning is more accurate than supervised learning

c)

Supervised learning is used for regression tasks, while unsupervised learning is used for classification tasks

d)

Unsupervised learning requires human intervention, while supervised learning does not

31.

Which type of machine learning algorithm is best suited for image recognition?

a)

Supervised Learning

b)

Unsupervised Learning

c)

Reinforcement Learning

d)

Deep Learning

32.

__________________ algorithms enable the computers to learn from past experiences and improve their performance over time.

a)

Artificial Intelligence

b)

Machine Learning

c)

Deep Learning

d)

Traditional Learning

33.

Which of the following is not a type of neural network architecture?

a)

Convolutional Neural Network

b)

Recurrent Neural Network

c)

Feedforward Neural Network

d)

Decision Tree Neural Network

34.

What is the main goal of Machine Learning?

a)

To make computers think like humans

b)

To automate tasks without programming

c)

To predict outcomes based on data

d)

To create intelligent robots

35.

Which type of Machine Learning algorithm is used for clustering data?

a)

Supervised Learning

b)

Unsupervised Learning

c)

Reinforcement Learning

d)

Deep Learning

36.

What is the role of neural networks in Deep Learning?

a)

To mimic the human brain and improve learning

b)

To process data faster than traditional algorithms

c)

To reduce the need for labeled data

d)

To make predictions without training

37.

What is the main difference between Machine Learning and Deep Learning?

a)

Machine Learning requires labeled data, while Deep Learning does not

b)

Deep Learning is a subset of Machine Learning

c)

Machine Learning is more complex than Deep Learning

d)

Deep Learning is a type of neural network

38.

Which type of learning algorithm is best suited for image recognition tasks?

a)

Supervised Learning

b)

Unsupervised Learning

c)

Reinforcement Learning

d)

Deep Learning

39.

What is the role of activation functions in neural networks?

a)

To determine the learning rate

b)

To normalize the input data

c)

To introduce non-linearity

d)

To calculate the loss function

40.

What is the primary difference between supervised and unsupervised learning?

a)

Supervised learning requires labeled data, while unsupervised learning does not

b)

Unsupervised learning is more accurate than supervised learning

c)

Supervised learning is used for regression tasks, while unsupervised learning is used for classification tasks

d)

Unsupervised learning requires human intervention, while supervised learning does not

41.

Which type of learning algorithm is best suited for image recognition tasks?

a)

Supervised Learning

b)

Unsupervised Learning

c)

Reinforcement Learning

d)

Deep Learning

42.

What is the role of activation functions in neural networks?

a)

To determine the learning rate

b)

To normalize the input data

c)

To introduce non-linearity

d)

To calculate the loss function

43.

What type of learning algorithm is best suited for detecting patterns in data?

a)

Supervised Learning

b)

Unsupervised Learning

c)

Reinforcement Learning

d)

Deep Learning

44.

Which category of algorithm allows computers to make decisions based on trial and error?

a)

Supervised Learning

b)

Unsupervised Learning

c)

Reinforcement Learning

d)

Deep Learning

45.

What is the primary goal of using Machine Learning algorithms?

a)

To automate tasks without programming

b)

To predict outcomes based on data

c)

To create intelligent robots

d)

To make computers think like humans

46.

What type of learning algorithm is best suited for natural language processing tasks?

a)

Supervised Learning

b)

Unsupervised Learning

c)

Reinforcement Learning

d)

Deep Learning

47.

Which type of neural network architecture is commonly used for image segmentation tasks?

a)

Convolutional Neural Network

b)

Recurrent Neural Network

c)

Feedforward Neural Network

d)

Decision Tree Neural Network

48.

What is the primary goal of reinforcement learning algorithms?

a)

To classify data into different categories

b)

To predict outcomes based on input data

c)

To maximize rewards by taking actions in an environment

d)

To group similar data points together

49.

What is the purpose of regularization in machine learning models?

a)

To increase bias and reduce variance

b)

To reduce bias and increase variance

c)

To prevent overfitting by penalizing large coefficients

d)

To speed up the training process

50.

Which type of neural network is commonly used for time series forecasting?

a)

Convolutional Neural Network

b)

Recurrent Neural Network

c)

Feedforward Neural Network

d)

Decision Tree Neural Network

51.

What is the main difference between k-means clustering and hierarchical clustering?

a)

K-means is a supervised learning algorithm, while hierarchical clustering is unsupervised

b)

K-means requires the number of clusters as input, while hierarchical clustering does not

c)

Hierarchical clustering is a distance-based algorithm, while k-means is a centroid-based algorithm

d)

K-means is more computationally expensive than hierarchical clustering

52.
Which technique involves rearranging data samples to evaluate a model's generalizability?
a)
Cross-validation
b)
Bootstrap
c)
Information Criterion
d)
Structural Risk Minimization
53.
Which stage of Machine Learning involves using the prepared data to build a model?
a)
Training
b)
Testing
c)
Preprocessing
d)
Prediction
54.
What kind of learning algorithm for "Future stock prices or currency exchange rates"?
a)
Recognizing Anomalies
b)
Prediction
c)
Generating Patterns
d)
Recognition Patterns
55.
In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called
a)
Deep learning
b)
Machine learning
c)
Unsupervised learning
d)
Reinforcement learning
56.
Which statement accurately describes the relationship between machine learning and deep learning?
a)
Machine learning is a subset of deep learning.
b)
Deep learning is a subset of machine learning.
c)
Machine learning and deep learning are unrelated concepts.
d)
Deep learning is a superset of machine learning.
57.
What type of data can deep learning work with, which machine learning cannot?
a)
Only structured data
b)
Only unstructured data
c)
Both structured and semi-structured data
d)
Both structured and unstructured data
58.
Which of the following are application of Deep Learning?
a)
Video captioning
b)
Visual question answering
c)
Video summarization
d)
All of the above
59.
How many layers Deep learning algorithms are constructed?
a)
2
b)
3
c)
4
d)
5
60.
Which of the following is a subset of machine learning?
a)
SciPy
b)
Numpy
c)
Deep learning
d)
All of the above
61.
The Deep Learning first layer is called the?
a)
hidden layer
b)
ouput layer
c)
input layer
d)
None of the above
62.
Which type of activation function is threshold-based and outputs either 1 or 0 depending on whether the input value is above or below a certain threshold?
a)
Linear activation function
b)
Non-linear activation function
c)
Binary step activation function
d)
Rectified linear unit (ReLU) activation function
63.
__________________________ is a branch of machine learning that uses data, loads and loads of data, to teach computers how to do things only humans were capable of before.
a)
Supervised Learning
b)
Deep Learning
c)
Unsupervised Learning
d)
None of these
64.
A _______________is divided into multiple layers and each layer is further divided into several blocks called nodes.
a)
Neural Networks
b)
Convolutional Neural Network (CNN)
c)
Machine learning algorithm
d)
Hidden Layers
65.
What is an artificial neural network?
a)
A programming language used for web development.
b)
A type of computer virus.
c)
A method for organizing files on a computer.
d)
A computational model inspired by the structure and function of biological neural networks in the brain.
66.
Which activation function is commonly used to introduce non-linearity in neural networks?
a)
Linear activation function
b)
ReLU (Rectified Linear Unit)
c)
Sigmoid activation function
d)
Step function
67.
Which of the following options correctly expands the abbreviation CNN?
a)
Complex Neural Network
b)
Convolutional Neutral Network
c)
Computer Neural Network
d)
Convolutional Neural Network
68.
CNN is mostly used when there is an?
a)
structured data
b)
unstructured data
c)
Both A and B
d)
None of the above
69.
Which of the following is/are Common uses of RNNs?
a)
Provide a caption for images
b)
Object detection:
c)
Face recognition
d)
All of the Above
70.
Which of the following is a type of neural network?
a)
Convolutional Neural Network (CNN)
b)
Decision Tree
c)
Naive Bayes
d)
K-Nearest Neighbors (KNN)
71.
Which is the correct structure of a Neural Network?
a)
Output, Hidden Layer, Input
b)
Hidden Layer, Input, Output
c)
Input, Hidden Layer, Output
d)
None of these
72.
Which of the following statements about the feedback connection in RNNs is true?
a)
The feedback connection allows the network to forget past information
b)
The feedback connection helps in remembering past information
c)
RNNs do not utilize feedback connections
d)
The feedback connection only affects the first time step of the network
73.
What is the difference between a convolutional neural network (CNN) and a recurrent neural network (RNN)?
a)
CNN is designed for image recognition tasks, while RNN is designed for sequential data processing
b)
CNN uses pooling layers, while RNN does not
c)
RNN is more computationally efficient compared to CNN
d)
CNN is more complex compared to RNN
74.
What does the stage of "Making sense of your ML model" primarily involve?
a)
Gathering more data for training
b)
Evaluating the performance of the trained model
c)
Refining the model architecture
d)
Cleaning the data
75.
What is the main purpose of deploying a machine learning model?
a)
To gather more data
b)
To train the model using efficient algorithms
c)
To make predictions available to users or other systems
d)
To clean and preprocess the data
76.
What are hyperparameters in machine learning?
a)
Parameters that are learned during training
b)
Parameters that control the behavior of the machine learning algorithm
c)
Parameters that are determined by the dataset
d)
Parameters that are fixed and cannot be changed
77.
What is the purpose of tuning hyperparameters in machine learning?
a)
To adjust the model's predictions
b)
To increase the size of the dataset
c)
To improve the model's performance
d)
To visualize the data
78.
During which stage of machine learning model development is the model trained on a dataset?
a)
Tuning hyperparameters
b)
Evaluating the model
c)
Choosing a machine learning algorithm
d)
Training the model
79.
How is the performance of a trained machine learning model typically evaluated?
a)
By using the same dataset for training and evaluation
b)
By comparing the model's predictions with the ground truth on a separate holdout dataset
c)
By increasing the number of hyperparameters
d)
By visualizing the data

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