Fundamentals of AI and ML
What AI, ML, and deep learning are, how models learn from data, and where AI creates real value. This domain builds the vocabulary the rest of the exam depends on.
Fundamentals of AI and ML is the opening domain of the AWS Certified AI Practitioner course, and it earns that spot by giving you the words everything else is built from. Before you can compare Bedrock to SageMaker, reason about prompt attacks, or judge whether a model is fair, you need a clear picture of what AI, machine learning, and deep learning actually are and how they differ.
This domain starts with core definitions and how models learn from data, then moves into where AI is a good fit and where it is not, and finishes with the lifecycle that turns an idea into a running model.
What This Domain Covers
- the difference between AI, machine learning, deep learning, and neural networks
- generative and agentic AI, and the kinds of data models learn from
- supervised, unsupervised, and reinforcement learning, and the main types of inferencing
- when AI is the right tool and when a simpler approach wins
- common problem types like regression, classification, and clustering, plus real AWS managed AI services
- the AI/ML development lifecycle, from pipeline to MLOps, and the model and business metrics that measure success
Why It Matters
Later domains quietly assume you already speak this language. A question about choosing a foundation model, securing a pipeline, or spotting bias is much harder to answer if you are still unsure what training data does or how inference differs from training. Get these fundamentals right and a large share of exam questions become reasoning problems instead of guessing games.
This domain also sets a pattern the whole certification returns to: AWS handles the heavy infrastructure of AI so teams can focus on picking the right approach, feeding it good data, and measuring whether it actually helped the business.