Question 1

When would you typically use the iloc attribute as opposed to the loc attribute in a pandas DataFrame?

Options :

A : When you want to apply a lambda function to each row

B : When you want to drop missing values from the DataFrame

C : When you want to access rows and columns by label

D : When you want to access rows and columns by integer-location

Answer: D

Question 2

You are given a JSON file containing social data about users' interactions on a website. The JSON object includes an array named likes that should contain integers representing the number of likes each post has received. Which of the following Python code snippets would be most appropriate to validate that all elements in the likes array are indeed integers?

Options :

A : import json

data = json.load(open('data.json'))

if len(data['likes']) > 0:

print("Data is valid")

else:

print("Data is invalid")

B : import json

data = json.load(open('data.json'))

if all(isinstance(x, int) for x in data['likes']):

print("Data is valid")

else:

print("Data is invalid")

C : import json

data = json.loads('data.json')

if isinstance(data['likes'], list):

print("Data is valid")

else:

print("Data is invalid")

D : import json

data = json.load(open('data.json'))

if 'likes' in data.keys():

print("Data is valid")

else:

print("Data is invalid")

Answer: B

Question 3

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 4

Your Python script is taking significantly longer than expected to complete. Which Python standard library could provide you with granular insights into the time taken by different parts of your code?

Options :

A : os

B : cProfile

C : sys

D : pickle

Answer: B

Question 5

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

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