Question 1

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 2

You are working with a dataset that has a mix of numerical and textual data. Your job is to synthesize this information into a more digestible form. What technique could you use to handle both types of data effectively for downstream analysis?

Options :

A : Run a Principal Component Analysis (PCA) on the entire dataset.

B : Implement a Decision Tree algorithm to identify the most important features.

C : Use k-Nearest Neighbors (k-NN) to classify data points.

D : Utilize Natural Language Processing (NLP) for textual data and Min-Max scaling for numerical data.

Answer: D

Question 3

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 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

Suppose you are working on a classification problem with an imbalanced dataset. Which of the following techniques would not be effective for addressing the issue?

Options :

A : Using accuracy as the evaluation metric

B : Using the Synthetic Minority Over-sampling Technique (SMOTE)

C : Using different evaluation metrics such as F1-score.

D : Under-sampling the majority class

Answer: A

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