Predictive-AI-Eng Exam Information and Outline
Predictive AI Engineer
Syllabus Update Checked on: November 5, 2026
Predictive-AI-Eng Exam Syllabus & Study Guide
Before you start practicing with our exam simulator, it is essential to understand the
official Predictive-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 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: Advanced Predictive AI
- Supervised and unsupervised learning techniques
- Feature encoding, imputation, dimensionality reduction and data wrangling
- Model evaluation, baseline modeling, optimization and transfer learning
- 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: Advanced Predictive AI Engineering
- Data preparation for neural networks: encoding, scaling, text representation
- Training patterns: CNNs, LSTMs, self-organizing maps
- Model optimization, retraining and transfer learning
- Module 5: Predictive AI Engineering Lab
- Case-study lab exercises applying predictive AI engineering