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GIAC GMLE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning for Cybersecurity | 15% | - Threat hunting and behavioral analytics - Malware analysis and classification - Security monitoring and anomaly detection |
| Topic 2: Python for Machine Learning | 15% | - Data science libraries (Pandas, NumPy, Matplotlib) - Machine learning frameworks (Scikit-learn, TensorFlow, PyTorch) - Scripting and automation for security data |
| Topic 3: Supervised Machine Learning | 15% | - Feature engineering and selection - Model training, validation and evaluation - Classification and regression algorithms |
| Topic 4: Unsupervised Machine Learning | 12% | - Clustering and dimensionality reduction - Anomaly detection techniques - Pattern recognition in security data |
| Topic 5: Data Acquisition, Preparation and Exploration | 15% | - Exploratory data analysis and visualization - Data collection methods (SQL, web scraping, APIs) - Data cleaning, transformation and normalization |
| Topic 6: Deep Learning and Neural Networks | 13% | - Convolutional Neural Networks (CNN) - Neural network fundamentals - Autoencoders and generative models |
| Topic 7: Statistics and Probability for Data Science | 15% | - Statistical testing and hypothesis testing - Probability theory and distributions - Descriptive and inferential statistics |
GIAC Machine Learning Engineer Sample Questions:
Which strategies can be applied to improve the anomaly detection performance of an autoencoder?
(Choose two)
Response:
- A. Reduce the number of hidden layers
- B. Add regularization techniques such as L1 or L2
- C. Increase the size of the latent space
- D. Train the autoencoder on both normal and anomalous data
Correct Answer: B,D 🗳️
When scraping web data for machine learning, which of the following should you consider to ensure data quality?
Response:
- A. Scraping only structured data
- B. Ensuring data is collected regularly and follows copyright laws
- C. Avoiding the use of any API
- D. Collecting as much data as possible without validation
Correct Answer: B 🗳️
Which of the following are common activation functions used in neural networks?
(Choose two)
Response:
- A. Weight initialization
- B. Batch normalization
- C. ReLU (Rectified Linear Unit)
- D. Sigmoid function
Correct Answer: C,D 🗳️
In the context of random forests, what is bagging?
Response:
- A. A technique that reduces overfitting by using a combination of decision trees trained on different subsets of the data
- B. A clustering algorithm that groups similar data points
- C. A method used to select the best hyperparameters for a model
- D. A process that reduces the dimensionality of the dataset
Correct Answer: A 🗳️
Which of the following is a data manipulation technique commonly applied to prepare data for machine learning models?
Response:
- A. Hyperparameter tuning
- B. Overfitting
- C. Data augmentation
- D. Normalization
Correct Answer: D 🗳️
