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Valid GMLE Test Practice | Reliable GMLE Learning Materials

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GIAC Machine Learning Engineer Sample Questions (Q66-Q71):

NEW QUESTION # 66
What is the primary use of 'k-fold cross-validation' in machine learning?
Response:

A. To optimize the hyperparameters of the model
B. To assess the model's performance on different subsets of the data
C. To increase the size of the training dataset
D. To select the most important features of the dataset

Answer: B

NEW QUESTION # 67
How does 'L1 regularization' differ from 'L2 regularization' in machine learning?
Response:

A. L1 optimizes the learning rate; L2 optimizes the batch size
B. L1 adds the absolute value of coefficients; L2 adds the square of coefficients
C. L1 is for neural networks; L2 is for decision trees
D. L1 is used for regression; L2 is used for classification

Answer: B

NEW QUESTION # 68
In the context of random forests, what is bagging?
Response:

A. A technique that reduces overfitting by using a combination of decision trees trained on different subsets of the data
B. A method used to select the best hyperparameters for a model
C. A clustering algorithm that groups similar data points
D. A process that reduces the dimensionality of the dataset

Answer: A

NEW QUESTION # 69
P-values in hypothesis testing are used to:
Response:

A. Estimate the probability of observing the data given the null hypothesis is true
B. Visualize the distribution of data
C. Calculate the mean of the data sample
D. Determine the practical significance of results

Answer: A

NEW QUESTION # 70
You are using a CNN to classify images in a dataset. After several epochs of training, you notice that the model performs well on the training set but poorly on the validation set. This suggests overfitting.
What steps should you take to improve the generalization of the model?
Response:

A. Reduce the size of the dataset and increase the number of convolutional layers
B. Train the model for more epochs and reduce the use of regularization
C. Remove padding to simplify the architecture
D. Implement dropout in the fully connected layers, apply data augmentation to increase the variability of the training data, and consider early stopping to prevent overfitting

Answer: D

NEW QUESTION # 71
......

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