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AAS 02 - Anomaly Detection

Total questions: 15

Worksheet time: 10mins

Name
Class
Date
1.

This picture shows an application of ...

a)

Supervised Learning: Classification

b)

Unsupervised Learning: Clustering

c)

Unsupervised Learning: Prediction

d)

Supervised Learning: Regression

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 are the three types of Machine Learning? Choose three.

a)

Supervised Learning

b)

Learning Differentiated

c)

Unsupervised Learning

d)

Reinforcement Learning

e)

Technical Learning

4.

What are the two types of Unsupervised Learning?

a)

Loitering

b)

Clustering

c)

Blind Signal Separation

d)

Dissociation

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.

TF-IDF penalizes heavily words that occur very frequently across multiple documents – true or false

a)

True

b)

False

7.

Which of the following cannot be used for time-series forecasting?

a)

Temporal Convolutional Network

b)

Multivariate regressor

c)

Recurrent Neural Network

d)

Autoencoder Neural Network

8.

An Intrusion Detection System (IDS) .....

a)

can be configured to allow the intruder IP when an alert is generated

b)

opening the network connection for an active and passive attack

c)

inspects network activities and identifies suspicious patterns that may indicate a network attack

d)

an identifier for the correct usage of particular computer or total network

9.

Goals of IDS

a)

Mobility and allow for a stable connection

b)

Take action and allowing an attack to the network

c)

Identify abnormal behaviour of network or misuse of resources

d)

Different ways to transmit data securely and safely

10.

Select the correct option.
A. Supervised learning methods include autoencoders.
B. The output and input of the autoencoder are identical.

a)
  1. Both the statements are TRUE.

b)
  1. Statement A is TRUE, but statement B is FALSE.

c)
  1. Statement A is FALSE, but statement B is TRUE.

d)
  1. Both the statements are FALSE.

11.

Autoencoders are trained without supervision.

a)

True

b)

False

c)

Cannot be predicted

d)

may be

12.

______________ is a recommended model for pattern recognition in unlabelled data

a)

CNN

b)

RNN

c)

Autoencoders

d)

Shallow neural networks

13.

Autoencoders cannot be used for Dimensionality Reduction.

a)

True

b)

False

c)

May be

d)

Cannot say

14.

Select the true statements

a)

we can use clustering algorithms to discover implicit grouping within the data

b)

Autoencoders is a supervised learning algorithm

c)

k-means is Density-based clustering

15.

The goal of clustering a set of data is to

a)

divide them into groups of data that are near each other

b)

choose the best data from the set

c)

determine the nearest neighbors of each of the data

d)

predict the class of data