GIAC (SANS)GIAC Machine Learning Engineer
The GIAC Machine Learning Engineer (GMLE) certification validates a practitioner's ability to apply practical data science, statistics, probability, and machine learning to real-world cybersecurity challenges. Designed for SOC analysts, security engineers, and data scientists, it proves you can build and deploy ML-driven tools for threat hunting, security monitoring, and anomaly detection. Earning GMLE demonstrates deep fluency in the realities of ML in the security landscape, strengthening your SOC with hands-on, performance-based validation.
417 practice questions · Updated 2026-07-30
GMLE Curriculum
Every domain, objective, and concept the GMLE exam measures.
- Data Acquisition Sources
- Data Extraction Techniques
- Data Ingestion Pipelines
- Data Quality Assessment
- Data Versioning and Provenance
- Python environment setup
- Core Python syntax
- Data structures
- NumPy basics
- Pandas for data manipulation
- Data visualization with Matplotlib and Seaborn
- Scikit-learn for machine learning
- Jupyter notebooks
- Probability Basics
- Conditional Probability
- Bayes' Theorem
- Probability Distributions
- Frequency and Relative Frequency
- Expected Value
- Variance and Standard Deviation
- Descriptive Statistics
- Inferential Statistics
- Probability Distributions
- Sampling Methods
- Hypothesis Testing
- Correlation and Causation
- Bayesian Statistics
- Statistical Significance
- Confidence Intervals
- Regression Analysis
- Clustering Fundamentals
- Distance and Similarity Measures
- K-Means Clustering
- Hierarchical Clustering
- Density-Based Clustering
- Gaussian Mixture Models (GMM)
- Cluster Evaluation
- Practical Considerations
- Linear Regression
- Polynomial Regression
- Regularization Techniques
- Logistic Regression
- Model Evaluation for Regression
- Supervised Learning Fundamentals
- Training and Testing Data
- Regression vs Classification
- Common Supervised Learning Algorithms
- Model Evaluation Metrics
- Overfitting and Underfitting
- Anomaly Detection Fundamentals
- Statistical Anomaly Detection Methods
- Machine Learning-Based Anomaly Detection
- Anomaly Detection Evaluation Metrics
- Optimization Fundamentals
- Gradient-Based Optimization
- Hyperparameter Optimization
- Regularization and Overfitting Control
- Anomaly Detection Optimization Strategies
- CNN Architecture Fundamentals
- Convolution Operation
- Activation Functions in CNNs
- Pooling Layers
- Flattening and Fully Connected Layers
- Training CNNs
- Regularization and Overfitting in CNNs
- CNN Hyperparameters
- Transfer Learning and Pretrained Models
- CNN Applications
- Neural Network Fundamentals
- Forward Propagation
- Loss Functions
- Backpropagation
- Optimization Algorithms
- Activation Functions
- Regularization Techniques
- Hyperparameter Tuning
- Model Evaluation and Validation
Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for GMLE, so none is invented.