Question 1

Suppose you have a DataFrame df with columns Name, Age, Gender, and Salary. Which of the following code snippets will filter the DataFrame to include only rows where Age is more than 25 and Salary is less than 50000, and also sort the resulting DataFrame by Name?

Options :

A : df[(df['Age'] > 25) & (df['Salary'] < 50000)].sort_values('Name')

B : df.query('Age > 25 and Salary < 50000').sort('Name')

C : df.sort_values('Name').where(df['Age'] > 25 & df['Salary'] < 50000)

D : df.filter('Age' > 25 & 'Salary' < 50000).sort_values(by='Name')

Answer: A

Question 2

You're using a Decision Tree classifier to predict whether a transaction is fraudulent. After training, you find that the classifier has a high accuracy on the training set but performs poorly on the test set. Which of the following techniques is most suitable to improve the model's performance?

Options :

A : Use a different kernel function.

B : Prune the decision tree.

C : Apply grid search without cross-validation.

D : Change the evaluation metric to accuracy.

Answer: B

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

Consider the Python code snippet below:

numbers = [1, 2, 3, 4, 5]

filtered_numbers = list(filter(lambda x: x % 2 == 1, numbers))

result = len(filtered_numbers)

What will be the value of result after executing the code?

Options :

A : 3

B : 4

C : 1

D : 5

E : 2

Answer: A

Question 5

You have a dataset of a retail company's sales transactions, which includes details like customer ID, transaction amount, transaction date, and store ID. You're tasked with summarizing monthly revenue for each store. What would be the most appropriate data aggregation technique?

Options :

A : Group the data by store ID and sum the transaction amounts daily, then take the average to get the monthly revenue.

B : Group the data by customer ID and store ID, then sum the transaction amounts and divide by the number of unique customers for each month.

C : Group the data by transaction date and calculate the daily revenue, then aggregate this to the monthly level without considering store ID.

D : Group the data by store ID and sum the transaction amounts for each month.

Answer: D

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