Exam Room · AI Practitioner

A Domain-by-Domain Checklist for the AI Practitioner Exam

· 7 min read

AI Fundamentals · part of The Exam Room

The whole AI Practitioner track, sorted into the five scored domains. This is the foundational certification, so the track assumes no AWS AI background and defines its terms as it goes. Where a subject also appears in the professional-level Generative AI Developer track, the version here is the one pitched at this exam.

How to use this

Read each domain heading and its scope line, then run down the list. The order inside each domain is deliberate: every list starts at the deciding-level idea and works toward its refinements, so the post above you is the one the post below leans on. A line you can explain out loud, tick. A line that makes you hesitate is the next hour of revision. The quizzes and cards are the fastest way to close a gap.

The five scored domains and their weight:

Domain Weight
1. Fundamentals of AI and ML 20%
2. Fundamentals of Generative AI 24%
3. Applications of Foundation Models 28%
4. Guidelines for Responsible AI 14%
5. Security, Compliance, and Governance for AI Solutions 14%

Weight is where the marks are, not where the difficulty is. Domain 3 is over a quarter of the score on its own.

Domain 1: Fundamentals of AI and ML (20%)

About 5 h 29 min of reading.

What machine learning is, what it is for, and how a model gets from data to production.

Anchor cheat sheet: ML Fundamentals and the SageMaker Suite · 41 min

What the words mean

About 1 h 29 min of reading.

Where AI earns its keep

About 1 h 39 min of reading.

From data to a model in production

About 1 h 40 min of reading.

Domain 2: Fundamentals of Generative AI (24%)

About 4 h 38 min of reading.

What a foundation model is, what it can and cannot do, and which AWS services build on one.

Anchor cheat sheet: Generative AI Foundations · 13 min

Generative AI groundwork

About 1 h 41 min of reading.

The AWS GenAI surface

About 1 h 08 min of reading.

Fit, limits, and value

About 1 h 32 min of reading.

What generative AI is good for

About 4 min of reading.

Domain 3: Applications of Foundation Models (28%)

About 5 h 37 min of reading.

Designing on top of a model: prompts, retrieval, customisation, and how you tell whether it works.

Anchor cheat sheet: AWS AI Services · 23 min

Retrieval and vector stores

About 34 min of reading.

Training and customisation

About 1 h 12 min of reading.

Prompt engineering

About 1 h 08 min of reading.

Designing on foundation models

About 1 h 13 min of reading.

Evaluating models and applications

About 1 h 07 min of reading.

Domain 4: Guidelines for Responsible AI (14%)

About 4 h 49 min of reading.

Bias, fairness, transparency and explainability, and the tools AWS gives you for each.

Anchor cheat sheet: Responsible AI, Security, and Governance · 12 min

Data, bias, and variance

About 1 h 51 min of reading.

Transparency and explainability

About 1 h 25 min of reading.

Responsible AI features and tools

About 1 h 21 min of reading.

Domain 5: Security, Compliance, and Governance for AI Solutions (14%)

About 5 h 23 min of reading.

Securing the system, proving where data came from, and the evidence a regulator asks for.

Securing the AI system

About 1 h 22 min of reading.

Governance, compliance, and evidence

About 2 h 04 min of reading.

Grounding and output accuracy

About 35 min of reading.

Data provenance and secure data

About 1 h 22 min of reading.

These posts are LLM-aided. Backbone, original writing, and structure by Craig. Research and editing by Craig + LLM. Proof-reading by Craig.