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GIAC GMLE Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Statistics and Probability | - Inferential statistics
|
| Topic 2: Python for Machine Learning | - Python scripting
|
| Topic 3: Anomaly Detection and Optimization | - Security-focused ML applications
|
| Topic 4: Machine Learning Fundamentals | - Supervised and unsupervised learning
|
| Topic 5: Data Acquisition and Exploration | - Data acquisition
|
| Topic 6: Neural Networks and Deep Learning | - Deep learning concepts
|
GIAC Machine Learning Engineer Sample Questions:
Question #1
How does 'L1 regularization' differ from 'L2 regularization' in machine learning?
Response:
A. L1 is for neural networks; L2 is for decision trees
B. L1 is used for regression; L2 is used for classification
C. L1 adds the absolute value of coefficients; L2 adds the square of coefficients
D. L1 optimizes the learning rate; L2 optimizes the batch size
Question #2
In machine learning, what is 'model validation'?
Response:
A. The process of training the model
B. The method of improving the model's performance
C. The technique of testing the model on unseen data
D. The practice of combining multiple models
Question #3
Which of the following are important when using standard deviation to understand data spread?
(Choose two)
Response:
A. Standard deviation is not influenced by outliers
B. Standard deviation can only be used for categorical data
C. A small standard deviation means that data points are close to the mean
D. A large standard deviation means that data points are more spread out
Question #4
The term 'convolution' in CNNs refers to:
Response:
A. A technique for accelerating network training
B. The process of combining multiple neural networks
C. A specialized neural network layer for image processing
D. A function applied to reduce the size of the data
Question #5
In a CNN, what is the purpose of the pooling layer?
Response:
A. To normalize the input data
B. To increase the resolution of the input image
C. To add non-linearity to the model
D. To combine features and reduce the dimensionality of the data
Solutions:
| Question #1 Answer: C | Question #2 Answer: C | Question #3 Answer: C,D | Question #4 Answer: C | Question #5 Answer: D |
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