AWS Certified AI Practitioner

What the exam covers

Listed as the issuer lists them, with weights where the issuer publishes them.

• ›Fundamentals of AI and ML (20%)
• ›Fundamentals of generative AI (24%)
• ›Applications of foundation models (28%)
• ›Guidelines for responsible AI (14%)
• ›Security, compliance and governance for AI solutions (14%)

Key terms to know

Words from this certification's topic areas, in plain English. Know these and the study material will be much easier to follow.

Machine learning

Software that learns patterns from examples instead of following hand-written rules.

Supervised learning

Learning from examples that already come with the right answer.

Overfitting

When a model memorises its training examples and does badly on new ones.

Foundation model

A large model trained on broad data that can be adapted to many tasks.

Large language model (LLM)

A foundation model that reads and writes text.

Prompt engineering

Writing instructions so a model gives better answers.

Hallucination

When a model states something false in a confident way.

Retrieval-augmented generation (RAG)

Looking up relevant documents first, then giving them to the model to answer from.

Fine-tuning

Training an existing model further on your own examples.

Embedding

A list of numbers that captures the meaning of text, so similar meanings end up close together.

Guardrails

Rules and filters that block unsafe inputs or outputs.

Bias

Unfair skew in a model's results, often caused by skewed training data.

Responsible AI

Building and using AI fairly, safely, transparently and with accountability.

Shared responsibility model

The cloud provider secures the cloud itself. You secure what you put in it.

Amazon Bedrock

An AWS service for using foundation models through an API.

Amazon SageMaker AI

An AWS service for building, training and deploying machine learning models.

Build the knowledge here

Our lessons are not exam prep for this certification, but they cover the same ground.