Question 1

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

Question 2

While evaluating a logistic regression model using a confusion matrix, you notice that the True Positive rate is significantly higher than the True Negative rate. What does this observation imply?

Options :

A : The model is better at identifying the positive class than the negative class

B : The model performs equally well for both classes

C : The model has a high level of bias

D : The model is overfitting

Answer: A

Question 3

When using Python's requests library to collect data from a website through HTTP requests, which status code indicates that the requested resource has been moved permanently?

Options :

A : 403

B : 404

C : 200

D : 301

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

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

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