Question 1

You are working with a large dataset containing multi-dimensional features related to customer behavior. The dataset is too large and complex to analyze easily. What technique could be most effective for reducing the dataset's dimensionality while retaining most of its original variance?

Options :

A : Principal Component Analysis (PCA)

B : One-hot encoding

C : Linear Discriminant Analysis (LDA)

D : Max-Min Scaling

Answer: A

Question 2

You have a Pandas DataFrame that contains missing values. Which of the following methods will replace all NaNs with zeros?

Options :

A : df.fillna(value=0)

B : df.replace(to_replace=np.nan, value=0)

C : df['column_name'] = 0

D : df.dropna(subset=['column_name'])

Answer: A

Question 3

You are given a DataFrame df with a column 'Date' of type string in the format 'YYYY-MM-DD'. You want to filter the rows where the year is 2020. Which of the following code snippets is the most efficient way to do so?

Options :

A : df[pd.to_datetime(df['Date']).dt.year == 2020]

B : df[df['Date'].str.contains('2020')]

C : df[df['Date'].str.slice(0, 4) == '2020']

D : df[df['Date'].apply(lambda x: x.split('-')[0]) == '2020']

Answer: C

Question 4

You need to collect data from an Excel file with multiple sheets. One of the sheets is named "Sales" and another is named "Inventory". You want to load the "Sales" sheet into a DataFrame named df_sales and the "Inventory" sheet into a DataFrame named df_inventory. Which of the following code snippets accomplishes this?

Options :

A : xls = pd.Excel('file.xlsx')

df_sales = xls.parse('Sales')

df_inventory = xls.parse('Inventory')

B : df_sales = pd.read_excel('file.xlsx', sheet_name='Sales')

df_inventory = pd.read_excel('file.xlsx', sheet_name='Inventory')

C : df_sales = pd.read_csv('file.xlsx', sheet_name='Sales')

df_inventory = pd.read_csv('file.xlsx', sheet_name='Inventory')

D : df_sales = pd.read_excel('file.xlsx').parse('Sales')

df_inventory = pd.read_excel('file.xlsx').parse('Inventory')

Answer: B

Question 5

While working with a dataset, you notice some outliers that are not errors but extreme values that are relevant to your analysis. How should you handle them for ensuring the reliability and accuracy of your Python data analysis?

Options :

A : Ignore the outliers as they are not errors

B : Apply a log transformation

C : Replace the outliers with median values

D : Remove the outliers

Answer: B

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