Data Pre Processing QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

What are the different types of attributes?

Nominal

Ordinal

Spacial

All of the Above

Question: 2

Examples of Nominal can be:

ID Numbers, eye color, zip codes

Rankings, taste of potato chips, grades, height

Calendar dates, temperatures in celsius or Fahrenheit, phone numbers

The temperature in Kelvin, length, time, counts

Question: 3

Examples of Ordinal can be:

ID Numbers, eye color, zip codes

Rankings, taste of potato chips, grades, height

Calendar dates, temperatures in Celsius or Fahrenheit, phone numbers

Temperature in Kelvin, length, time, counts

Question: 4

Examples of Interval can be:

ID Numbers, eye color, zip codes

Rankings, taste of potato chips, grades, height

Calendar dates, temperatures in celsius or Fahrenheit

Temperature in Kelvin, length, time, counts

Question: 5

The type of a Nominal attribute depends on which of the following properties:

Distinctness & order

Distinctness, order & addition

Distinctness

All 4 properties

Question: 6

The type of an Interval attribute depends on which of the following properties:

Distinctness & order

Distinctness, order & addition

Distinctness

All 4 properties

Question: 7

The type of an Ordinal attribute depends on which of the following properties:

Distinctness & order

Distinctness, order & addition

Distinctness

All 4 properties

Question: 8

Important Characteristics of Structured Data are:

Generality

Dimensionality

Resolution

All of the Above

Question: 9

What are some examples of data quality problems:

Noise and outliers

Duplicate data

Missing values

All of the Above

Question: 10

Which Method is used for encoding the categorical variables?

LabelEncoder

OneHotEncoder

CategoryEncoder

All of the Above

Question: 11

Under fitting happens due to -

A fewer number of features

Data has a high variance

No use of regularization

All of the Above

Question: 12

Over fitting happens due to -

Imbalance in data

Noise in data

Data has a high variance

All of the Above

Question: 13

Why do we need feature transformation?

Converting non-numeric features into numeric

Resizing inputs to a fixed size

Both A and B

None

Question: 14

Which of the following is true about outliers -

Data points that deviate a lot from normal observations

Can reduce the accuracy of the model

Both A and B

None

Question: 15

Some of the Imputation methods are -

Imputation with mean/median

Imputing with random numbers

Imputing with one

All of the above

Question: 16

Which algorithm does not require feature scaling?

Naive Bayes

Decision Tree

B. Decision Tree

None

Question: 17

The purpose of feature scaling is to -

Accelerating the training time

Getting better accuracy

Both A and B

None

Question: 18

In standardization, the features will be rescaled with -

Mean 0 and Variance 0

Mean 0 and Variance 1

Mean 1 and Variance 0

Mean 1 and Variance 1

Question: 19

What is a Dummy Variable Trap?

Multicollinearity among the dummy variables

One variable predicts the value of other

Both A and B

None of the Above

Question: 20

Which of the following(s) is/are features scaling techniques?

Standardization

Normalization

Min-Max Scaling

All of the Above

Question: 21

The characteristic of a good dataset is-

Sufficiently large for getting meaningful predictions

The bias is lower

Both A and B

None

Question: 22

How to handle the missing values in the dataset?

Dropping the missing rows or columns

Imputation with mean/median/mode value

Taking missing values into a new row or column

All of the above

Question: 23

The correct way of pre processing the data should be-

Imputation ->feature scaling-> training

Feature scaling->imputation->training

Feature scaling->label encoding->training

None

Question: 24

Which one is a feature extraction example?

Constructing a bag of words model

Imputation of missing values

Principal component analysis

All of the Above

Question: 25

Which of these techniques is used for normalization in text mining?

Stemming

Stop words removal

Lemmatization

All of the above

Question: 26

What stemming refers to in text mining?

Reducing a word to its root

Defining the parts of speech of a word

Converting sentences to words

None

Question: 27

Which is the correct order for pre processing in Natural Language Processing?

tokenization ->stemming ->lemmatization

lemmatization ->tokenization ->stemming

stemming ->tokenization ->lemmatization

None

Question: 28

Bag of Words in text pre processing is a-

Feature scaling technique

Feature extraction technique

Feature selection technique

None

Question: 29

In text mining, how the words ‘lovely’ is converted to ‘love’-

By stemming

By tokenization

By lemmatization

None

Question: 30

Stop words are-

Words frequently found in a document

Words not important for text mining

Words having no use in prediction

All of the Above

Data Pre Processing QUIZ (MCQ QUESTIONS AND ANSWERS)

TIME:20:00

Time:20:00

What are the different types of attributes?

Examples of Nominal can be:

Examples of Ordinal can be:

Examples of Interval can be:

The type of a Nominal attribute depends on which of the following properties:

The type of an Interval attribute depends on which of the following properties:

The type of an Ordinal attribute depends on which of the following properties:

Important Characteristics of Structured Data are:

What are some examples of data quality problems:

Which Method is used for encoding the categorical variables?

Under fitting happens due to -

Over fitting happens due to -

Why do we need feature transformation?

Which of the following is true about outliers -

Some of the Imputation methods are -

Which algorithm does not require feature scaling?

The purpose of feature scaling is to -

In standardization, the features will be rescaled with -

What is a Dummy Variable Trap?

Which of the following(s) is/are features scaling techniques?

The characteristic of a good dataset is-

How to handle the missing values in the dataset?

The correct way of pre processing the data should be-

Which one is a feature extraction example?

Which of these techniques is used for normalization in text mining?

What stemming refers to in text mining?

Which is the correct order for pre processing in Natural Language Processing?

Bag of Words in text pre processing is a-

In text mining, how the words ‘lovely’ is converted to ‘love’-

Stop words are-