AI AI Engineer: Machine Learning Fundamentals and Algorithms 3

Question 1

What is the curse of dimensionality and how does it affect machine learning models?

Accepted Answer

High-dimensional data requires exponentially more training examples to maintain statistical coverage of the feature space

Answer

Models with many hyperparameters always overfit regardless of training data size

Answer

Adding more features always improves model performance due to increased information

Answer

Deep learning models cannot process inputs with more than 1,000 features

Question 2

What is transfer learning in the context of machine learning?

Accepted Answer

Reusing a model pre-trained on one task as a starting point for a different but related task

Answer

Moving a trained model from a development environment to a production server

Answer

Transferring knowledge between team members by sharing model documentation

Answer

Converting a model trained on one data format to accept a different input format

Question 3

What does the ROC-AUC score measure in a binary classification model?

Accepted Answer

The probability that the model ranks a randomly chosen positive example higher than a randomly chosen negative example

Answer

The average squared difference between predicted probabilities and true labels

Answer

The proportion of predictions that match the true class label across all thresholds

Answer

The ratio of true positives to the total number of actual positive examples

Question 4

Which of the following is an example of a generative machine learning model?

Accepted Answer

Variational Autoencoder (VAE)

Answer

Logistic Regression

Answer

Support Vector Machine

Answer

Random Forest

Question 5

What is early stopping as a regularization technique during model training?

Accepted Answer

Halting training when validation loss stops improving to prevent overfitting

Answer

Reducing the learning rate exponentially after a fixed number of epochs

Answer

Removing neurons with the lowest activation values after each epoch

Answer

Truncating the gradient computation at a maximum value to prevent exploding gradients

Question 6

In the context of decision trees, what is information gain?

Accepted Answer

The reduction in entropy (uncertainty) in the target variable achieved by splitting on a given feature

Answer

The increase in model accuracy after adding a new feature to the training data

Answer

The total number of correctly classified examples after a split

Answer

The difference between the training accuracy and the validation accuracy at each tree depth

Question 7

What is the primary difference between a parametric and a non-parametric machine learning model?

Accepted Answer

Parametric models assume a fixed functional form with a set number of parameters, while non-parametric models grow in complexity with the training data

Answer

Parametric models require GPU acceleration, while non-parametric models run on CPUs

Answer

Parametric models can only handle continuous features, while non-parametric models handle categorical features

Answer

Parametric models use gradient descent, while non-parametric models use Bayesian inference