AI-GE-Specialist Exam Information and Outline
AI Governance & Ethics Specialist
Syllabus Update Checked on: November 10, 2026
AI-GE-Specialist Exam Syllabus & Study Guide
Before you start practicing with our exam simulator, it is essential to understand the
official AI-GE-Specialist 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 Predictive AI
- Business and technology drivers, benefits and risks
- Predictive AI types and business problem categories
- Learning approaches: supervised, unsupervised, semi-supervised, reinforcement
- Model training loop; AI models and neural networks
- Functional designs: computer vision, NLP, speech recognition, robotics
- Module 2: 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 3: Fundamental Agentic AI
- Agent basics: perceiving, reasoning and acting; agent-environment loop
- Agent architectures: reflex, model-based, goal-based, utility-based
- LLMs, knowledge graphs and system prompts
- Human-agent collaboration and human-in-the-loop
- Module 4: Fundamental AI Governance & Ethics
- Fairness, transparency, accountability and explainability
- Bias detection and mitigation
- Data governance, training data quality and lineage
- Ethics across the AI lifecycle and risk assessment frameworks
- Module 5: Advanced AI Governance & Ethics
- Production data governance and drift monitoring
- Oversight and auditing of agentic workflows
- Regulatory alignment, codes of conduct and incident response
- Human-in-the-loop vs. human-on-the-loop oversight
- Module 6: AI Governance & Ethics Lab
- Case-study exercises applying AI governance and ethics