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GIAC Machine Learning Engineer Sample Questions:

1. What does the term 'hyperparameter tuning' refer to?
Response:

A) Adjusting the model parameters based on training data
B) Selecting the optimal learning rate for training
C) Reducing the number of parameters in a neural network
D) Choosing the best set of parameters for the learning algorithm

2. 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 optimizes the learning rate; L2 optimizes the batch size
C) L1 adds the absolute value of coefficients; L2 adds the square of coefficients
D) L1 is used for regression; L2 is used for classification

3. Which of the following are key characteristics of genetic algorithms used in neural network optimization?
(Choose two)
Response:

A) Random initialization of weights
B) Fitness evaluation based on a predefined objective
C) Evolution through mutation and crossover
D) Manual selection of hyperparameters

4. What is the main challenge in determining the optimal number of clusters for a k-means algorithm?
Response:

A) It depends on the amount of training data
B) It needs feature scaling before clustering
C) It increases the computational complexity
D) It requires a predefined number of clusters, which may not reflect the true structure of the data

5. Stochastic Gradient Descent differs from traditional Gradient Descent by:
Response:

A) Using a fixed learning rate throughout the training process
B) Updating model parameters after evaluating each data point
C) Updating model parameters after evaluating the entire dataset
D) Eliminating the need for a learning rate

Solutions: