Question 1

Which of the following techniques should be avoided to ensure that your Python data scripting code is easily understandable and follows best practices?

Options :

A : Using descriptive variable names

B : Refactoring repetitive code into reusable functions

C : Documenting functions with docstrings

D : Using single-letter variable names for main variables

Answer: D

Question 2

Which of the following libraries in Python is not commonly used for web scraping to collect data?

Options :

A : Scrapy

B : Selenium

C : Pandas

D : BeautifulSoup

Answer: C

Question 3

You are working on a machine learning project where you need to build a model for image classification. The project also requires heavy numerical computations and data preprocessing. Which of the following Python libraries would be most suitable to complete your project successfully?

Options :

A : Seaborn, Statsmodels, OpenCV

B : Pandas, Matplotlib, Keras

C : Scikit-learn, Matplotlib, BeautifulSoup

D : NumPy, Scikit-image, TensorFlow

Answer: D

Question 4

You are analyzing the relationship between hours of study and exam scores among students. After calculating, you find a correlation coefficient (r) of 0.9. Which of the following is the best interpretation?

Options :

A : Hours of study has a strong, positive correlation with exam scores

B : Hours of study has a moderate, negative effect on exam scores

C : Exam scores have no relationship with hours of study

D : Hours of study causes high exam scores

Answer: A

Question 5

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

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