Make informed AWS AI decisions

Optional · not a prerequisite exam

Check your cloud foundations

If AWS terminology and shared responsibility are unfamiliar, review CLF-C02. You do not need to pass it to follow this learning path. Try our test to identify gaps or move straight to the role-specific material.

Core learning

Build understanding, one area at a time

Use the official exam guide as your complete coverage checklist. These study blocks organize your preparation; they are not a replacement for the full blueprint.

1. 1.AI and ML concepts, suitable business use cases, and the limits of model output.
2. 2.Generative AI and foundation-model applications, including evaluation and model-selection considerations.
3. 3.Responsible AI, security, compliance, governance, and the risks of sensitive data in AI systems.

Hands-on consolidation

Put it into practice

Write a proposal for an internal document assistant using synthetic documents. Compare a simple search approach with a generative AI approach, identify possible incorrect answers, define evaluation examples, and describe data-access and human-review controls.

Use isolated labs and synthetic data, never production. Check current prices before creating resources. Budget alerts do not cap spending; remove resources and backups you no longer need.

Practice and review

Find gaps with our AIF-C01 test

Try the questions, explain why each option is right or wrong, and return to the relevant official resource. Repeat practical work where your understanding is weak.

Practice percentages are learning feedback, not AWS scaled scores or a pass guarantee. Verify availability, recommended experience, and booking requirements on AWS before scheduling.

Choose your next direction

AI foundations can lead to more technical AI and machine learning work. Our catalog does not yet contain a follow-on AWS ML engineering practice set.