Question 1

Suppose you are working on a classification problem with an imbalanced dataset. Which of the following techniques would not be effective for addressing the issue?

Options :

A : Using accuracy as the evaluation metric

B : Using the Synthetic Minority Over-sampling Technique (SMOTE)

C : Using different evaluation metrics such as F1-score.

D : Under-sampling the majority class

Answer: A

Question 2

You have a linear regression model with 5 features. After checking the VIF (Variance Inflation Factor) for multicollinearity, one of the features has a VIF of 12. What should you consider doing?

Options :

A : Increase the sample size to improve the model.

B : Remove the feature with the high VIF from the model.

C : Use L1 regularization to mitigate the effect of multicollinearity.

D : Ignore it, as VIF of 12 is acceptable.

Answer: B

Question 3

You have a dataset with 15 features and you want to visualize it on a 2D plot for exploratory data analysis. What would be the first step in using PCA (Principal Component Analysis) to achieve this?

Options :

A : Perform clustering on the dataset

B : Apply a linear regression model to reduce dimensions

C : Use a scatter plot to visualize all the features

D : Standardize the dataset

Answer: D

Question 4

You are analyzing the relationship between hours of study and exam scores among students. After calculating, you find a correlation coefficient (r) of 0.9. Which of the following is the best interpretation?

Options :

A : Hours of study has a strong, positive correlation with exam scores

B : Hours of study has a moderate, negative effect on exam scores

C : Exam scores have no relationship with hours of study

D : Hours of study causes high exam scores

Answer: A

Question 5

Which of the following libraries in Python is not commonly used for web scraping to collect data?

Options :

A : Scrapy

B : Selenium

C : Pandas

D : BeautifulSoup

Answer: C

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