Question 1

What is the purpose of removing duplicate values in a dataset?

Accepted Answer

To ensure data integrity and avoid biased results

Answer

To increase the size of the dataset

Answer

To improve the model’s accuracy

Answer

To reduce the dataset’s complexity

Question 2

What is the main purpose of handling outliers in data?

Accepted Answer

To make data more consistent and improve model performance

Answer

To remove irrelevant data

Answer

To increase the size of the dataset

Answer

To reduce data noise

Question 3

Which operation is used to combine two or more datasets?

Accepted Answer

Merging

Answer

Splitting

Answer

Encoding

Answer

Filtering

Question 4

What is the primary purpose of filtering data?

Accepted Answer

To remove irrelevant or noisy data

Answer

To generate new features

Answer

To change the data types

Answer

To increase the size of the dataset

Question 5

How do you fill missing values in a Pandas DataFrame with the mean of the column?

Accepted Answer

df.fillna(df.mean( ))

Answer

df.fillna('mean')

Answer

df.nanfill(df.mean( ))

Answer

df.replaceNaN(df.mean( ))

Question 6

Which statistic is used to measure the central tendency of a dataset?

Accepted Answer

Mean

Answer

Range

Answer

Standard deviation

Answer

Variance

Question 7

What is the mode of a dataset?

Accepted Answer

The value that appears most frequently

Answer

The average of the values

Answer

The middle value when sorted

Answer

The range between the highest and lowest values

Question 8

What is the purpose of normalization in feature engineering?

Accepted Answer

To convert data to a common scale without distorting differences

Answer

To remove outliers from the data

Answer

To reduce the size of the dataset

Answer

To convert categorical data into numerical format

Question 9

What is the difference between normalization and standardization?

Accepted Answer

Normalization fixed range, standardization to mean of 0 and SD of 1

Answer

There is no difference between normalization and standardization

Answer

Normalization is used for categorical, standardization used for numerical

Answer

Normalization removes outliers, while standardization does not

Question 10

How do you encode categorical variables into numerical values (One Hot) using pandas?

Accepted Answer

pd.get_dummies(df)

Answer

pd.transform()

Answer

df.to_numeric( )

Answer

df.encode( )

Question 11

What is the purpose of feature encoding in machine learning?

Accepted Answer

To convert categorical data into a numerical format

Answer

To remove irrelevant features

Answer

To scale numerical features

Answer

To handle missing data

Question 12

What type of plot is commonly used to visualize the distribution of a single variable?

Accepted Answer

Histogram

Answer

Scatter plot

Answer

Pie chart

Answer

Box plot

Question 13

What’s the best way to handle missing values in a dataset?

Accepted Answer

Replace them with “abracadabra.”

Answer

Fill them with chocolate syrup for sweetness.

Answer

Let them go; they clearly needed a break.

Answer

Politely ask them to come back.

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