AI AI Engineer: Machine Learning Fundamentals and Algorithms 2

Question 1

What is the kernel trick in Support Vector Machines (SVM)?

Accepted Answer

A technique that implicitly maps data to a higher-dimensional space to find a linear separator

Answer

A method to reduce training time by approximating the decision boundary

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A regularization method that penalizes support vectors far from the margin

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A pruning strategy that removes irrelevant features before training

Question 2

What is the primary difference between gradient boosting and bagging (e.g., Random Forest)?

Accepted Answer

Gradient boosting builds models sequentially where each model corrects the errors of the previous one

Answer

Bagging builds models sequentially, while gradient boosting builds them in parallel

Answer

Bagging is only applicable to regression tasks, while gradient boosting handles classification

Answer

Gradient boosting averages predictions, while bagging uses voting to select the best model

Question 3

Why is feature standardization (zero mean, unit variance) important before applying algorithms like SVM or k-nearest neighbors?

Accepted Answer

It prevents features with larger numerical ranges from dominating distance-based calculations

Answer

It reduces the number of training iterations required for convergence

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It ensures the model produces probabilistic outputs between 0 and 1

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It automatically removes irrelevant features from the dataset

Question 4

What does Principal Component Analysis (PCA) accomplish?

Accepted Answer

It reduces dimensionality by projecting data onto orthogonal axes of maximum variance

Answer

It selects the most important features based on their correlation with the target variable

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It clusters data points into groups based on their proximity in feature space

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It generates synthetic training samples to balance imbalanced datasets

Question 5

In k-means clustering, how is the optimal number of clusters (k) typically determined?

Accepted Answer

By using the elbow method, which plots inertia vs. k and identifies the point of diminishing returns

Answer

By using the default value of k=3 as it works for most datasets

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By setting k equal to the square root of the number of data points

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By running the algorithm with k=1 and incrementally adding clusters until accuracy stops improving

Question 6

What is the purpose of a validation set, distinct from both the training set and test set?

Accepted Answer

To tune hyperparameters and select the best model without contaminating the final test evaluation

Answer

To provide additional data for training when the dataset is small

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To evaluate the final model performance and report results to stakeholders

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To detect data drift after a model is deployed to production

Question 7

Which of the following statements best describes a naive Bayes classifier?

Accepted Answer

It applies Bayes' theorem assuming conditional independence between features given the class label

Answer

It builds a decision boundary by maximizing the margin between classes

Answer

It iteratively adjusts weights to minimize the cross-entropy loss between predictions and labels

Answer

It partitions the feature space into rectangular regions using a series of threshold decisions