A team is training a neural network for image segmentation. The loss function is the cross-entropy loss. The model is underfitting, and the team wants to increase model capacity without causing overfitting. Which approach is most appropriate?

Reduce the batch size to introduce more gradient noise.

Increase the number of layers and add dropout.

Add L2 regularization to the loss function.

Increase the learning rate to speed up convergence.

Which machine learning approach for anomaly detection is best suited when you have a dataset that contains only normal examples and no labeled anomalies?

Supervised learning with synthetic anomaly generation

Supervised classification with a balanced dataset

Unsupervised learning using an isolation forest

Semi-supervised learning with a few labeled anomalies

GIAC Machine Learning Engineer

Domain 3Objective 1

Anomaly Detection and Optimization GMLE Practice Questions (Page 3)

Part of the Advanced Machine Learning and Neural Networks domain, which makes up ~30% of our current practice bank. GIAC (SANS) does not publish an official question count, but from its 180-minute exam (~70–120 total, ~21–36 in this domain), expect 7–12 from this objective — we provide 47 practice questions to prepare you well beyond it. (estimate)

47questions here

10free pages

9concepts

Questions 11–15

11foundation · easy
Which regularization technique adds a penalty term proportional to the absolute value of the weights to the loss function, encouraging sparsity in the model?
Select an answer first
12expert · hard
A team is training a neural network for image segmentation. The loss function is the cross-entropy loss. The model is underfitting, and the team wants to increase model capacity without causing overfitting. Which approach is most appropriate?
Select an answer first
13foundation · easy
Which machine learning approach for anomaly detection is best suited when you have a dataset that contains only normal examples and no labeled anomalies?
Select an answer first
14foundation · easy
Which evaluation metric for anomaly detection measures the area under the curve that plots the true positive rate against the false positive rate at various threshold settings?
Select an answer first
15foundation · easy
Which hyperparameter optimization method is most efficient when the hyperparameter space is large and the evaluation of each configuration is expensive?
Select an answer first

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Anomaly Detection and Optimization GMLE Practice Questions (Page 3)

Questions 11–15

Which regularization technique adds a penalty term proportional to the absolute value of the weights to the loss function, encouraging sparsity in the model?

A team is training a neural network for image segmentation. The loss function is the cross-entropy loss. The model is underfitting, and the team wants to increase model capacity without causing overfitting. Which approach is most appropriate?

Which machine learning approach for anomaly detection is best suited when you have a dataset that contains only normal examples and no labeled anomalies?

Which evaluation metric for anomaly detection measures the area under the curve that plots the true positive rate against the false positive rate at various threshold settings?

Which hyperparameter optimization method is most efficient when the hyperparameter space is large and the evaluation of each configuration is expensive?

Keep studying — Advanced Machine Learning and Neural Networks