Question 1

When debugging a complex Python script for data analysis, which of the following practices is least recommended?

Options :

A : Ignoring warnings as long as the script does not crash

B : Writing unit tests to verify each function

C : Inserting print statements at different points in the script

D : Using a debugger to step through the code

Answer: A

Question 2

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 3

Your dataset has multiple variables with different scales and units, making it challenging to compare them directly. You need to synthesize this data into a digestible report for decision-makers who are not data scientists. What should be your first step in preprocessing the data?

Options :

A : Perform natural language processing (NLP) on any text variables

B : Transform all variables into Z-scores

C : Conduct a chi-squared test for each variable

D : Apply hierarchical clustering to group similar variables

Answer: B

Question 4

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 5

Your dataset includes several categorical variables. What is the most efficient way to convert these variables into a format that can be fed into a machine learning model?

Options :

A : Use label encoding indiscriminately

B : Apply one-hot encoding selectively

C : Convert all categories to their alphabetical rank

D : Use frequency encoding for all categorical variables

Answer: B

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