AI and ML Core Concepts

This topic builds the vocabulary the rest of the AWS Certified AI Practitioner exam depends on. Before you can compare services, reason about prompts, or judge whether a model is fair, you need a clear picture of what AI, machine learning, deep learning, and generative AI are, how they nest inside each other, and how a model goes from raw data to a live prediction. Every term here appears again in later domains, so getting them right early turns much of the exam into reasoning instead of guessing.

What This Topic Covers

• The difference between AI, machine learning, and deep learning, and the core vocabulary of model, algorithm, training, and inference
• What neural networks are, what makes deep learning "deep," and how it compares to traditional ML
• Generative AI, foundation models, LLMs, and agentic AI, and how each differs from predictive ML
• The two independent properties of data (structure and labeling) and common formats like tabular, time-series, image, and text
• The three ways models learn: supervised, unsupervised, and reinforcement learning
• The four ways a trained model serves predictions: real-time, batch, asynchronous, and serverless inference

Why It Matters

These concepts map almost one-to-one onto Task Statement 1.1 of the exam, which asks you to define AI terms, compare AI, ML, deep learning, generative AI, and agentic AI, and describe the types of data, learning, and inferencing. The exam tests the boundaries between these ideas far more than the definitions themselves: whether you can tell generative from agentic AI, labeled from unlabeled data, or real-time from batch inference in a described scenario. Master the distinctions here and the rest of the course, from foundation model applications to security and governance, rests on solid ground.