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GIAC Machine Learning Engineer Sample Questions (Q144-Q149):

NEW QUESTION # 144
What is the purpose of the backpropagation algorithm in neural networks?
Response:

A. To initialize weights and biases
B. To generate the final predictions from the model
C. To prevent overfitting in the model
D. To update the model parameters based on the error gradient

Answer: D

NEW QUESTION # 145
You are analyzing the performance of machine learning models by comparing their error rates. After calculating the mean and standard deviation of the errors for each model, you notice that one model has a high standard deviation compared to the others.
What does this suggest about the model's performance, and what steps can you take to improve it?
Response:

A. The model's performance is optimal, and no changes are needed
B. Increase the dataset size without making any changes to the model
C. The mean error is sufficient to evaluate performance, and standard deviation can be ignored
D. The high standard deviation suggests that the model's errors are inconsistent; you should apply regularization or tune hyperparameters to reduce variability

Answer: D

NEW QUESTION # 146
You are tasked with building a classification model to predict whether customers will purchase a product based on their browsing behavior. You decide to use a Random Forest classifier. After training the model, you notice that the training accuracy is very high, but the validation accuracy is much lower.
What might be causing this, and how can you improve the model's generalization?
Response:

A. The training data is too large; reducing the training set size will improve the model's performance
B. The model is likely overfitting to the training data; applying cross-validation and reducing the number of trees in the forest or using regularization techniques can help improve the generalization to unseen data
C. The features are not useful; removing all but one feature will reduce overfitting
D. The model is underfitting the data; increasing the number of features and trees will improve performance

Answer: B

NEW QUESTION # 147
What is the role of the centroid in the k-means clustering algorithm?
Response:

A. It serves as the initial seed for the clustering process
B. It is the geometric center of a cluster, representing the mean of the data points in that cluster
C. It represents the boundary between two clusters
D. It is the point that minimizes the error in each cluster

Answer: B

NEW QUESTION # 148
What is the primary benefit of using the 'early stopping' technique in training machine learning models?
Response:

A. To prevent overfitting by stopping the training when performance on a validation set starts to degrade
B. To increase the accuracy of the model on the training data
C. To speed up the training process by reducing the number of iterations
D. To optimize the hyperparameters automatically during training

Answer: A

NEW QUESTION # 149
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