Question 1

You are working with a logistic regression model for a binary classification problem. You notice that the training error is significantly lower than the validation error. What could you do to improve the model's performance on the validation set?

Options :

A : Increase the model-s learning rate.

B : Add more features to the model.

C : Remove all features and retrain the model.

D : Regularize the model by introducing L1 or L2 penalties.

Answer: D

Question 2

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 3

You are working on a project that requires real-time data collection from a WebSocket API. Which of the following Python libraries would be most appropriate for this task?

Options :

A : csv

B : websocket-client

C : urllib

D : scrapy

Answer: B

Question 4

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 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