Question 1

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 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 building a data pipeline that requires you to load data from an API, transform it, and then save it to a database. Which of the following is the best approach to structure your script for maintainability and reusability?

Options :

A : Use global variables to hold the data as it moves through the pipeline.

B : Implement all operations as nested loops inside a main loop.

C : Write the entire workflow in a single function.

D : Separate the loading, transforming, and saving operations into individual functions.

Answer: D

Question 4

You discover that a text-based column in your DataFrame supposed to contain only alphanumeric characters has special characters in some entries. This constitutes "bad data" for your specific use-case. How can you most effectively clean this column?

Options :

A : Convert the entire DataFrame to a NumPy array and handle text cleaning

B : Use Regular Expressions with df.str.replace() to remove all special characters at once

C : Use df.replace() to replace each special character individually

D : Drop the column to avoid any complications

Answer: B

Question 5

Your Python script is giving you unexpected results in data analysis. Which of the following is generally not advisable for debugging this issue?

Options :

A : Review and validate data inputs and transformations

B : Use conditional breakpoints to isolate the problem

C : Examine the Python traceback to identify where the error originates

D : Suppress all exceptions using a general except block

Answer: D