AWS AI and ML certification path
Start with AIF-C01 if you are new to AI, then take MLA-C02 if you build and operate models for a living. If you already work as an ML engineer, skip AIF-C01 entirely and go straight to MLA-C02 — the foundational exam will teach you nothing you do not already use daily.
The two exams
| Exam | Level | Assumes | Take it if |
|---|---|---|---|
| AIF-C01 | Foundational | No hands-on ML experience | You work alongside AI projects and need to understand them |
| MLA-C02 | Associate | Real experience building and deploying models | You are an ML or data engineer |
There is no enforced prerequisite. AWS does not require AIF-C01 before MLA-C02.
Who AIF-C01 is actually for
The AWS Certified AI Practitioner is aimed at people who are near AI work without doing it: product managers, analysts, consultants, sales engineers, and developers moving into the area. Roughly half the exam covers AI and generative AI fundamentals — what a foundation model is, what embeddings do, why a model hallucinates — plus applications of foundation models, responsible AI, and security and governance.
It is a conceptual exam. You will not be asked to write code or size an instance. You will be asked which approach suits a scenario and why.
If that describes a gap you have, it is a good exam and a short one. If you already build RAG pipelines, it is a certificate for knowledge you already hold.
What MLA-C02 demands
The Machine Learning Engineer Associate is a different proposition. Its four domains carry almost equal weight: data preparation, model and foundation model development, deployment and orchestration, and operating, monitoring and securing ML solutions.
That last pair matters. Three-quarters of this exam is about getting models into production and keeping them working — drift, retraining triggers, canary deployments, batch versus real-time serving, least-privilege access for training jobs. Only a quarter is model building.
This is why reading alone rarely passes it. The questions describe production situations and ask which response survives contact with reality. If you have never watched a model degrade over six months or debugged training-serving skew, the reasoning behind the right answers will feel arbitrary.
Should you take both?
If you are an engineer: no. Take MLA-C02. The foundational certificate adds little to a CV that already claims ML engineering, and nobody senior will be impressed by it alongside the associate.
If you are not an engineer: take AIF-C01 and stop there. MLA-C02 is not a stretch goal, it is a different job. Attempting it without production experience is an expensive way to discover that.
If you are moving from analysis into engineering: AIF-C01 first is reasonable, because it will structure knowledge you have picked up unevenly. Then give yourself six months of real work before MLA-C02.
How long each takes
- AIF-C01 — three to four weeks of consistent study for someone without hands-on experience
- MLA-C02 — six to eight weeks for a working ML engineer, considerably longer without that background
AWS or Azure?
If you already work on one cloud, certify on that one. A certification in the platform you do not use is worth very little, because you cannot back it up in an interview.
If you genuinely have a choice, the more useful comparison is between the exams at the same level rather than between the vendors. AIF-C01 and AI-901 occupy similar ground at foundational level, while MLA-C02 and AI-103 are both associate exams but point in different directions — MLA-C02 towards classical ML operations, AI-103 towards generative AI and agents.
What to do next
Read the exam format guide for the exam you have chosen, then take the free 20-question practice test in that section. Under fourteen correct means the domain guides are a better use of your next week than booking the exam.
Comparing against Azure? See AIF-C01 vs AI-901 and MLA-C02 vs AI-103. The exam changes tracker lists current versions across vendors.