Use this quick start guide to collect all the information about GIAC GMLE Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the GIAC Machine Learning Engineer (GMLE) exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual GIAC Machine Learning Engineer (GMLE) certification exam.
The GIAC GMLE certification is mainly targeted to those candidates who want to build their career in Cyber Defense domain. The GIAC Machine Learning Engineer (GMLE) exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of GIAC GMLE.
GIAC GMLE Exam Summary:
| Exam Name | GIAC Machine Learning Engineer (GMLE) |
| Exam Code | GMLE |
| Exam Price | $979 (USD) |
| Duration | 180 mins |
| Number of Questions | 82 |
| Passing Score | 65% |
| Books / Training | SEC595: Applied Data Science and AI/Machine Learning for Cybersecurity Professionals |
| Schedule Exam | Pearson VUE |
| Sample Questions | GIAC GMLE Sample Questions |
| Practice Exam | GIAC GMLE Certification Practice Exam |
GIAC GMLE Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Anomaly Detection and Optimization | - The candidate will demonstrate a fundamental understanding autoencoders and how they are used in anomaly detection problems. The candidate will also demonstrate a fundamental understanding of how genetic algorithms are applied to automate the optimization of neural networks. |
| Clustering | - The candidate will demonstrate a fundamental understanding of machine learning concepts such as clustering, and unsupervised machine learning. |
| Convolutional Neural Networks | - The candidate will demonstrate a fundamental understanding of how convolutional neural networks are used to solve classification problems as well as for predictive analytics. |
| Data Acquisition | - The candidate will demonstrate a fundamental understanding of data acquisition, cleaning, and manipulation terminology and the steps necessary to prepare threat data for additional threat hunting analysis. The candidate will demonstrate familiarity with accessing data from SQL, document stores, and by web scraping. |
| Leveraging Python | - The candidate will demonstrate a fundamental understanding of the Python scripting language and modules such as NumPy, Pandas, and TensorFlow and how to leverage them to extract, visualize, transform, and load data. |
| Neural Networks | - The candidate will demonstrate a fundamental understanding of deep learning concepts using neural networks for supervised machine learning. Candidates will demonstrate an understanding of loss and error functions, vectors, matrices and tensors. |
| Probability and Frequency | - The candidate will demonstrate a fundamental understanding of probability theory, inference, the Bayes theorem and Fourier series. |
| Regressions | - The candidate will demonstrate a fundamental understanding of regressions and their application in deep learning. |
| Statistics Fundamentals | - The candidate will demonstrate a fundamental understanding of statistics and how it is applied to data science for threat hunting use cases. The candidate will demonstrate familiarity with terminology such as mean, and median. |
| Supervised Learning | - The candidate will demonstrate a fundamental understanding of support vector classifiers, kernel functions, support vector machines, decision trees and random forests. |
To ensure success in GIAC GMLE certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for GIAC Machine Learning Engineer (GMLE) exam.