Question 1

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 2

What is the most appropriate technique to handle missing values for a categorical column in a dataset before fitting it to a model?

Options :

A : Remove Rows with Missing Values

B : Mean Imputation

C : Linear Regression Imputation

D : Mode Imputation

Answer: D

Question 3

You have a DataFrame named df that has three columns: 'A', 'B', 'C'. The DataFrame has 100 rows. Your task is to create a DataFrame where each column 'A', 'B', 'C' is transformed into 3 columns each: 'A_min', 'A_max', 'A_mean', 'B_min', 'B_max', 'B_mean', 'C_min', 'C_max', 'C_mean'. Which of the following code snippets accomplishes this task?

Options :

A : new_df = df.groupby(['A', 'B', 'C']).agg(['min', 'max', 'mean'])

B : new_df = df.apply(['min', 'max', 'mean'])

C : new_df = df.pivot(columns=['A', 'B', 'C'], values=['min', 'max', 'mean'])

D : new_df = df.aggregate(

{

'A': ['min', 'max', 'mean'],

'B': ['min', 'max', 'mean'],

'C': ['min', 'max', 'mean'],

}

)

Answer: D

Question 4

Your Python script is taking significantly longer than expected to complete. Which Python standard library could provide you with granular insights into the time taken by different parts of your code?

Options :

A : os

B : cProfile

C : sys

D : pickle

Answer: B

Question 5

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

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