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

What is the primary reason to use PCA (Principal Component Analysis) over t-SNE when visualizing high-dimensional data?

Options :

A : PCA is non-linear while t-SNE is linear

B : PCA is more computationally efficient for large datasets

C : PCA retains local structure while t-SNE focuses on global structure

D : PCA gives a more accurate representation of distances between clusters

Answer: B

Question 3

In Python, which of the following methods is not a common statistical approach to identify outliers within a dataset?

Options :

A : Interquartile Range (IQR)

B : Z-score

C : Tukey-s Fences

D : Mean Squared Error (MSE)

Answer: D

Question 4

You're fetching data from an API that returns data in JSON format. After fetching the data using Python's requests library, what should you do next to convert the returned data into a Python dictionary?

Options :

A : data_dict = response.json()

B : import json

data_dict = json.load(response.text)

C : import json

data_dict = json.loads(response.content)

D : data_dict = eval(response.text)

Answer: A

Question 5

You're working with a DataFrame df that contains the exam scores of students in three different classes: Math, Science, and History. You're interested in determining if the mean exam score significantly differs between these classes. What would be the most appropriate statistical test to use?

Options :

A : One-way ANOVA

B : Pearson Correlation Test

C : Paired t-test

D : Independent Samples t-test

Answer: A