AI-Consultant Exam Information and Outline
AI Consultant
Syllabus Update Checked on: November 10, 2026
AI-Consultant Exam Syllabus & Study Guide
Before you start practicing with our exam simulator, it is essential to understand the
official AI-Consultant 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 Predictive AI Engineering
- Model selection, hyperparameter tuning, deployment and monitoring
- Bias detection, explainability and validation
- Neural network components, activation functions and architectures
- Module 4: Fundamental Generative AI Engineering
- Data representation, latent space and prompt engineering
- Metrics for evaluating generative models
- Model selection, deployment, monitoring and bias mitigation
- Module 5: Fundamental AI Architecture & Design
- AI architecture vs. AI engineering; product vs. custom architectures
- Architecture scopes, operational modes and system types (monolithic, modular, hybrid)
- Core modules: data ingestion, preprocessing, feature engineering, inference engine, model repository
- Operations, data, model and ancillary monitors