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GIAC Machine Learning Engineer Sample Questions:

1. Which techniques can help prevent overfitting in Convolutional Neural Networks?
(Choose two)
Response:

A) Max pooling
B) Dropout
C) Early stopping
D) Increasing the number of convolutional layers

2. What is the primary challenge addressed by 'class imbalance' techniques in machine learning?
Response:

A) Reducing the dimensionality of the dataset
B) Optimizing the model for better accuracy
C) Improving the computational efficiency of the model
D) Handling datasets where some classes are much more frequent than others

3. What does 'gradient boosting' refer to in ensemble learning?
Response:

A) A technique to decrease the training time of models
B) An approach to reduce the variance of the model
C) A strategy to reduce the complexity of models
D) A method of combining weak learners to create a strong learner

4. Overfitting in supervised learning models refers to:
Response:

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

5. Which of the following are important when using standard deviation to understand data spread?
(Choose two)
Response:

A) A large standard deviation means that data points are more spread out
B) Standard deviation is not influenced by outliers
C) Standard deviation can only be used for categorical data
D) A small standard deviation means that data points are close to the mean

Solutions: