MLA-C02 vs AIF-C01: which AWS AI exam?
AWS has two AI certifications below the specialty tier, and the gap between them is wide. MLA-C02 is associate level for engineers who build and operate ML systems. AIF-C01 is foundational, for people who use AI on AWS without building it.
Neither is a prerequisite for the other.
Side by side
| MLA-C02 | AIF-C01 | |
|---|---|---|
| Level | Associate | Foundational |
| Passing score | 720 / 1000 | 700 / 1000 |
| Duration | 170 min (beta) | 90 min |
| Price | 75 USD (beta) | 100 USD |
| Question types | Multiple choice, multiple response | Plus ordering and matching |
| Expected experience | 1+ year with SageMaker AI and Bedrock, plus 1+ year in a related role | Up to 6 months exposure |
| You are expected to | Build, deploy and operate | Use and understand |
| Validity | 3 years | 3 years |
During beta, MLA-C02 is actually cheaper than AIF-C01 despite being the harder exam. That is temporary.
The domains show the split
MLA-C02 — engineering:
| Domain | Weight |
|---|---|
| Data Preparation for ML and AI | 28% |
| ML Model and FM Development | 24% |
| Deployment and Orchestration | 24% |
| Operating, Monitoring, and Securing | 24% |
AIF-C01 — understanding:
| Domain | Weight |
|---|---|
| Fundamentals of AI and ML | 20% |
| Fundamentals of GenAI | 24% |
| Applications of Foundation Models | 28% |
| Guidelines for Responsible AI | 14% |
| Security, Compliance, and Governance | 14% |
AIF-C01 asks which service fits a business problem. MLA-C02 asks how you ingest and transform the data, select and tune the model, deploy the endpoint, configure auto scaling, wire the CI/CD pipeline and monitor it in production.
The out-of-scope lists settle it
AWS publishes what each candidate is not expected to do.
Out of scope for AIF-C01: coding models, data and feature engineering, hyperparameter tuning, building pipelines, statistical analysis, implementing security, developing governance frameworks.
Out of scope for MLA-C02: architecting full end-to-end solutions, setting ML strategy, integrating a wide array of new tools, deep expertise in multiple ML domains.
Nearly everything excluded from AIF-C01 is squarely inside MLA-C02. Meanwhile MLA-C02’s exclusions are about seniority and breadth, not capability — that territory belongs to the specialty and architect tiers.
Which should you take?
Take MLA-C02 if you write code against SageMaker AI or Bedrock, prepare training data, deploy endpoints, build pipelines, or operate models in production. AWS’s one-year expectation is realistic rather than decorative — this exam is difficult without hands-on experience.
Take AIF-C01 if you are a product manager, analyst, consultant, or a developer who consumes AI services. Also take it first if you are new to AI on AWS and want a structured foundation — at 100 USD it is a cheap ramp.
Take both, in order, if you are moving from using AI to building it.
If you can only take one, and you qualify for both
Take MLA-C02. It is associate level, it signals capability rather than literacy, and it covers the foundational material along the way. AIF-C01’s value is highest for people who will not go further; if you are going further, it is a stepping stone you can skip.
The exception is timing: during beta, MLA-C02 costs 75 USD. That makes sitting both unusually affordable, and there is a reasonable argument for taking AIF-C01 as a warm-up if your experience is closer to the one-year line than comfortably past it.