AI AI Engineer: Machine Learning Fundamentals and Algorithms

Question 1

What does the bias-variance tradeoff describe in machine learning?

Accepted Answer

The balance between model complexity and generalization to unseen data

Answer

The relationship between training speed and model accuracy

Answer

The ratio of labeled to unlabeled data in a dataset

Answer

The tradeoff between precision and recall in classification

Question 2

Which regularization technique adds the sum of the absolute values of model weights as a penalty term to the loss function?

Accepted Answer

L1 (Lasso) regularization

Answer

L2 (Ridge) regularization

Answer

Dropout regularization

Answer

Elastic Net regularization

Question 3

What is the primary purpose of k-fold cross-validation?

Accepted Answer

To obtain a more reliable estimate of model performance by using all data for both training and validation

Answer

To increase the size of the training dataset through data augmentation

Answer

To reduce training time by splitting data into smaller batches

Answer

To select the optimal number of features for a model

Question 4

Which evaluation metric is most appropriate when classes are severely imbalanced and the cost of false negatives is high?

Accepted Answer

F1 Score

Answer

Accuracy

Answer

Mean Squared Error

Answer

R-squared

Question 5

What is the primary advantage of ensemble methods like Random Forest over a single decision tree?

Accepted Answer

They reduce variance by aggregating predictions from multiple models

Answer

They require less computational power to train

Answer

They are easier to interpret and visualize

Answer

They eliminate the need for feature engineering

Question 6

In gradient descent, what does the learning rate control?

Accepted Answer

The size of the steps taken toward the minimum of the loss function

Answer

The number of training epochs before the model converges

Answer

The proportion of data used in each training batch

Answer

The threshold for classifying a prediction as positive

Question 7

Which of the following best describes overfitting in a machine learning model?

Accepted Answer

The model performs well on training data but poorly on unseen test data

Answer

The model performs poorly on both training and test data

Answer

The model converges before reaching the global minimum of the loss function

Answer

The model requires too many features to make accurate predictions