Question 1

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 2

In a regression analysis of employee job satisfaction against years of experience, the p-value for the 'years of experience' variable is found to be 0.12. How should you interpret this result at a 0.05 significance level?

Options :

A : A p-value of 0.12 confirms that years of experience and job satisfaction are positively correlated.

B : Years of experience is not a statistically significant predictor of job satisfaction.

C : Years of experience is a statistically significant predictor of job satisfaction.

D : A p-value of 0.12 means that years of experience has a 12% impact on job satisfaction.

Answer: B

Question 3

You are tasked with merging two datasets, df1 and df2, on a common column 'id'. df1 has a column 'value1' and df2 has a column 'value2'. After merging, you need to create a new column called 'total_value' that sums 'value1' and 'value2'. Which of the following code snippets accomplishes this?

Options :

A : df_merged = pd.concat([df1, df2], keys='id')

df_merged['total_value'] = df_merged['value1'] + df_merged['value2']

B : df_merged = df1.merge(df2)

df_merged['total_value'] = df1['value1'] + df2['value2']

C : df_merged = df1.join(df2, on='id')

df_merged['total_value'] = df1['value1'] + df2['value2']

D : df_merged = pd.merge(df1, df2, on='id')

df_merged['total_value'] = df_merged['value1'] + df_merged['value2']

Answer: D

Question 4

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 5

Which of the following NumPy methods would you use to apply a function element-wise on a 1-D array, while maintaining the shape of the original array?

Options :

A : np.reshape()

B : np.apply_along_axis()

C : np.ravel()

D : np.vectorize()

Answer: D

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