AI-Architect Exam Format | Course Contents | Outline | Syllabus | Objectives

AI-Architect Exam Syllabus & Study Guide

Before you start practicing with our exam simulator, it is essential to understand the official AI-Architect 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.

MODULE 01 — Fundamental Predictive AI The following primary topics are covered: - Predictive AI Business and Technology Drivers - Predictive AI Benefits - Common Risks and Challenges of Using Predictive AI - Business Problem Categories Addressed by AI - Types of Predictive AI - Common Predictive AI Learning Approaches - Understanding Predictive AI Learning and Model Training - Step-by-Step Training Loop Process - Supervised Learning, Unsupervised Learning, Continuous Learning - Heuristic Learning, Semi-Supervised Learning, Reinforcement Learning - Common Predictive AI Functional Designs, Computer Vision, Pattern Recognition - Robotics, Natural Language Processing (NLP) - Speech Recognition, Natural Language Understanding (NLU) - Understanding AI Models and Neural Networks amazonaws MODULE 04 — Fundamental Generative AI The following primary topics are covered: - Generative AI Business and Technology Drivers - Generative AI Benefits - Common Risks and Challenges of Using Generative AI - Business Problem Categories Addressed by Generative AI - Understanding Models, Algorithms and Neural Networks - Types of Generative AI - Understanding Generative Adversarial Networks (GANs) - Understanding Variational Encoders (VAE) - Understanding Transformers - Steps to Building AI Systems - Generative AI Best Practices amazonaws MODULE 13 — Fundamental AI Architecture & Design The following primary topics are covered: - AI Architecture vs. AI Engineering Comparison - AI Product Architectures vs. Custom AI Architectures - AI Architecture Scopes (System and Solution) - AI Solution Operational Modes (Training and Production) - AI System Architecture Types (Monolithic, Modular, Hybrid) - AI Solution Data Storage (Internal, External, Hybrid) - AI System Core Modules - Data Ingestion for Common Predictive AI and Generative AI Data Sources - Data Preprocessing in Predictive AI and Generative AI Systems - Feature Engineering in Predictive AI and Generative AI Systems - Inference Engine in Predictive AI and Generative AI Systems - Model Repository in Predictive AI and Generative AI Systems - Operations Monitors (Performance, Resource) - Data Monitors (Input, Output) - Model Monitors (Weight and Gradient, Activation Distribution, Bias and Fairness) - Ancillary Monitors (Explainability, Robustness and Adversarial Attack, Data Quality, Data Labeling) amazonaws MODULE 14 — Advanced AI Architecture & Design The following primary topics are covered: - AI System Scalability Patterns - Distributed Data Processing and Data Caching - Data Partitioning and Sharding - Incremental Processing - Hardware Acceleration - Autoscaling and Load Balancing - Continuous Learning - AI System Performance Patterns - Parallelism and Concurrency - Edge Caching and Vectorization - Data Compression - Lazy Loading - AI System Resiliency Patterns - Fault Tolerance - Graceful Degradation - Chaos Engineering Core Terminologies: Learning Types: Supervised, Unsupervised, Semi-Supervised, Reinforcement, Heuristic, Continuous Learning, Neural Network Models, GANs, VAEs, Transformers, LLMs AI Functional Designs: Computer Vision, Pattern Recognition, NLP, NLU, Speech Recognition, Robotics Architecture Types: Monolithic, Modular, Hybrid Scopes & Modes: System Architecture, Solution Architecture, Training Mode, Production Mode Core System Modules: Data Ingestion, Data Preprocessing, Feature Engineering, Inference Engine, Model Repository Monitors: Performance, Resource, Input Data, Output Data, Weight & Gradient, Activation Distribution, Bias & Fairness, Explainability, Robustness, Data Quality, Data Labeling Scalability Patterns: Distributed Data Processing, Data Caching, Partitioning & Sharding, Incremental Processing, Hardware Acceleration, Autoscaling, Load Balancing, Performance Patterns, Parallelism, Concurrency, Edge Caching, Vectorization, Data Compression, Lazy LoadingResiliency PatternsFault Tolerance, Graceful Degradation, Chaos Engineering