AWS / Foundational / AIF-C01
AI Practitioner
Separate prediction, generation and agent actions. Remember model choices, evaluation, responsible AI and security.
THE REVISION PATH
Your topics, in order.
Read. Recall. Explain the alternative.
AI, Machine Learning and Service Selection
Predict a label, predict a number, find a group, or generate content: these are different jobs.
The ML Lifecycle and MLOps
Split before learning transformations; evaluate before deployment; monitor after deployment.
Foundation Models and Generative AI
A model predicts plausible output; your application must establish whether it is useful and supported.
Prompt Engineering, RAG and Fine-Tuning
Prompt for instructions, retrieve for current evidence, tune for learned behaviour.
Agents, Tools and AWS AI Platforms
The model proposes; tools act; policy decides whether an action is allowed.
Evaluation and Responsible AI
Measure the answer, the experience and the harm; averages can hide who fails.
AI Security, Privacy and Governance
Protect the data path and the action path, then keep evidence of both.
How this guide is organised
Original revision notes arranged around practical decisions. The linked official objectives define the mapped scope; primary documentation supports the explanations. Read each topic, answer without looking, then explain why another option would fail.
- Official exam guide ↗ Scope authority
- Stephane Maarek: AI Practitioner public course outline ↗ Topic-sequence reference; current official scope adds agentic AI
Revision material supports preparation; it does not guarantee every possible exam question. Check the exam version and official objectives before booking.