Question 1

What is the primary reason to use PCA (Principal Component Analysis) over t-SNE when visualizing high-dimensional data?

Options :

A : PCA is non-linear while t-SNE is linear

B : PCA is more computationally efficient for large datasets

C : PCA retains local structure while t-SNE focuses on global structure

D : PCA gives a more accurate representation of distances between clusters

Answer: B

Question 2

You have a Pandas DataFrame that contains missing values. Which of the following methods will replace all NaNs with zeros?

Options :

A : df.fillna(value=0)

B : df.replace(to_replace=np.nan, value=0)

C : df['column_name'] = 0

D : df.dropna(subset=['column_name'])

Answer: A

Question 3

You are given a DataFrame df with a column 'Date' of type string in the format 'YYYY-MM-DD'. You want to filter the rows where the year is 2020. Which of the following code snippets is the most efficient way to do so?

Options :

A : df[pd.to_datetime(df['Date']).dt.year == 2020]

B : df[df['Date'].str.contains('2020')]

C : df[df['Date'].str.slice(0, 4) == '2020']

D : df[df['Date'].apply(lambda x: x.split('-')[0]) == '2020']

Answer: C

Question 4

You are working on a machine learning project where you need to build a model for image classification. The project also requires heavy numerical computations and data preprocessing. Which of the following Python libraries would be most suitable to complete your project successfully?

Options :

A : Seaborn, Statsmodels, OpenCV

B : Pandas, Matplotlib, Keras

C : Scikit-learn, Matplotlib, BeautifulSoup

D : NumPy, Scikit-image, TensorFlow

Answer: D

Question 5

You have the following Pandas DataFrame with some missing values:

import pandas as pd

import numpy as np

df = pd.DataFrame(

{"A": [1, np.nan, 3], "B": [4, 5, np.nan], "C": [7, 8, 9]}

)

How would you fill the missing values in column 'A' with the mean value of that column?

Options :

A : df['A'] = df['A'].fillna(df['C'].mean())

B : df['A'].fillna(df['A'].mean(), inplace=True)

C : df.fillna({'A': df['A'].mean()})

D : df['A'].apply(lambda x: x.fillna(x.mean()))

Answer: B