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GIAC Machine Learning Engineer Sample Questions:

1. In a CNN, what is the purpose of the pooling layer?
Response:

A) To add non-linearity to the model
B) To combine features and reduce the dimensionality of the data
C) To normalize the input data
D) To increase the resolution of the input image

2. In deep learning, what is a 'neural network layer'?
Response:

A) The initial input data for the neural network
B) A set of algorithms for optimizing the model
C) A collection of neurons that process a set of input data
D) The final output of the neural network

3. Your cybersecurity team is tasked with detecting anomalies in network traffic that may indicate malicious activity. You decide to use an autoencoder for this task. After training the autoencoder on normal network traffic data, you notice that it is not accurately detecting anomalies.
What are the next steps you should take to improve the performance of the autoencoder?
Response:

A) Train the autoencoder solely on anomalous data to improve its accuracy
B) Increase the complexity of the network by adding more layers and disabling dropout
C) Adjust the size of the latent space and apply regularization techniques to reduce overfitting
D) Retrain the autoencoder with fewer data points and remove regularization techniques

4. What does 'one-hot encoding' do in the preprocessing of categorical data?
Response:

A) It identifies and removes outliers
B) It converts categorical variables into binary vectors
C) It reduces the dimensionality of the data
D) It scales all features to a uniform range

5. P-values in hypothesis testing are used to:
Response:

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

Solutions: