Generative-AI-Eng Exam Information and Outline
Generative AI Engineer
Syllabus Update Checked on: November 5, 2026
Generative-AI-Eng Exam Syllabus & Study Guide
Before you start practicing with our exam simulator, it is essential to understand the
official Generative-AI-Eng exam objectives. This course outline serves as your roadmap.
The information below reflects the 2026 syllabus defined by
Arcitura.
Below are complete topics detail with latest syllabus and course outline, that will help you good knowledge about exam objectives and topics that you have to prepare. These contents are covered in questions and answers pool of exam.
- Exam Format
- Online-proctored multiple-choice certification exam (Pearson VUE) covering the modules below
- Practice exam questions provided with the certification bundle
- Module 1: Fundamental Generative AI
- Business drivers, benefits, risks and problem categories
- Generative models, algorithms and neural networks
- GANs, VAEs and Transformers
- Steps to building AI systems and best practices
- Module 2: Advanced Generative AI
- Output control: noise and temperature; ethical oversight
- Pre-trained models and transfer learning
- GANs, VAEs, Transformers, cGANs and RNNs/LSTMs
- Large language models and NLP
- Model evaluation, optimization and avoiding overfitting
- Module 3: Fundamental Generative AI Engineering
- Data representation, latent space and prompt engineering
- Metrics for evaluating generative models
- Model selection, deployment, monitoring and bias mitigation
- Module 4: Advanced Generative AI Engineering
- Data wrangling, encoding and dimensionality reduction
- Training patterns: GAN-based image and LSTM sequence generation
- Content filtering, performance evaluation, retraining and reinforcement learning
- Module 5: Generative AI Engineering Lab
- Case-study lab exercises applying generative AI engineering