Data Science Essentials Quiz

What is the purpose of a Business Hypothesis in A/B testing?

• To analyze the outliers

• To determine the sample size

• To calculate the p-value

• To describe the desired impact for the business

Which technique is used to handle missing data in Machine Learning?

• Duplication

• Normalization

• Omission

• Interpolation

What is the main advantage of using Ensemble Learning in model building?

• Requires more training time

• Reduces variance

• Increases bias

• Decreases accuracy

What is the purpose of Dimensionality Reduction in Unsupervised Learning?

• Improve model generalization

• Introduce noise in the model

• Increase the number of features

• Speed up ML training

How is the optimal number of clusters determined in clustering analysis using the Silhouette method?

• By comparing the number of features in each cluster

• By using the Elbow method

• By selecting a random number of clusters

• By calculating the mean distance between each observation and all others in the same cluster

What is the purpose of K-fold cross-validation in machine learning?

• To prevent model overfitting

• To increase model complexity

• To reduce the number of features

• To speed up model training

What does the confusion matrix show in a classification model?

• Number of training iterations

• Number of correctly and incorrectly classified observations

• Number of hyperparameters tuned

• Number of features used

What is the main difference between Random Forest and Gradient Boosting models?

• Random Forest has a single decision tree

• Gradient Boosting builds multiple individual models

• Random Forest uses boosting technique

• Gradient Boosting does not use ensemble methods

What is the role of an AI Engineer compared to a Data Scientist?

• AI Engineers focus on data storytelling

• Data Scientists build AI systems

• AI Engineers work with structured data

• Data Scientists use generative AI

How does multicollinearity affect regression models?

• Improves interpretability

• Leads to overfitting

• Reduces variance

• Increases model accuracy