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GIAC Machine Learning Engineer Sample Questions:

1. Which techniques are commonly used to optimize neural networks during training?
(Choose two)
Response:

A) Softmax activation function
B) Adam optimizer
C) Stochastic Gradient Descent
D) Data augmentation

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

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

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

4. The 'Pandas' library in Python is primarily used for:
Response:

A) Real-time data streaming
B) Building neural network models
C) Data manipulation and analysis
D) Implementing reinforcement learning algorithms

5. Which functions are typically used for data manipulation in the Pandas library?
(Choose two)
Response:

A) tf.keras.models()
B) pd.DataFrame()
C) pd.merge()
D) plt.show()

Solutions: