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

NEW QUESTION # 144
Overfitting in supervised learning models refers to:
Response:

A. Models that are too simplistic to capture underlying patterns
B. The process of training models on large datasets
C. Models capturing noise in the training data as if it were a true signal
D. Models performing equally on training and test data

Answer: C

NEW QUESTION # 145
What is the purpose of 'regularization' in machine learning models?
Response:

A. To speed up the training process
B. To enhance the visualization of the model's performance
C. To add constraints to the model to reduce overfitting
D. To increase the number of features in the model

Answer: C

NEW QUESTION # 146
In deep learning, 'dropout' is a technique used to:
Response:

A. Prevent overfitting
B. Speed up computations
C. Increase model accuracy
D. Reduce data dimensionality

Answer: A

NEW QUESTION # 147
You are building a multiple linear regression model to predict employee salaries based on years of experience, education level, and performance ratings. After training the model, you notice that it is overfitting the training data.
What techniques should you use to reduce overfitting and improve the model's generalization?
Response:

A. Remove regularization and increase the number of training samples
B. Increase the number of features and training epochs
C. Focus only on the most recent data and ignore the training set
D. Apply L2 regularization (Ridge), reduce the complexity of the model, and increase the size of the training dataset

Answer: D

NEW QUESTION # 148
In machine learning, what is the primary goal of optimization algorithms?
Response:

A. To reduce the computational complexity
B. To improve the accuracy of predictions
C. To increase data storage efficiency
D. To enhance data visualization techniques

Answer: B

NEW QUESTION # 149
......

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