AIF-C01 vs MLA-C02: which AWS AI exam?
These are AWS’s two AI-focused certifications below the specialty tier, and they are separated by a wide gap. AIF-C01 is foundational, for people who use AI on AWS. MLA-C02 is associate, for engineers who build and operate it.
Neither is a prerequisite for the other. Pick by what you do.
Side by side
| AIF-C01 | MLA-C02 | |
|---|---|---|
| Level | Foundational | Associate |
| Certification | AWS Certified AI Practitioner | AWS Certified Machine Learning Engineer – Associate |
| Passing score | 700 / 1000 | 720 / 1000 |
| Scored questions | 50 (of 65) | 50 (of 65) |
| Question types | Multiple choice, multiple response, ordering, matching | Multiple choice, multiple response only |
| Expected experience | Up to 6 months exposure to AI/ML on AWS | 1+ year with SageMaker AI and Bedrock, plus 1+ year in a related role |
| You are expected to | Use AI/ML solutions | Build, deploy and operate them |
Note the passing scores differ. 700 for AIF-C01, 720 for MLA-C02 — a small but real difference in the bar.
The domains tell the story
AIF-C01 is about 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% |
MLA-C02 is about doing:
| Domain | Weight |
|---|---|
| Data Preparation for ML and AI | 28% |
| ML Model and Foundation Model Development | 24% |
| Deployment and Orchestration of ML and AI Workflows | 24% |
| Operating, Monitoring, and Securing ML and AI Solutions | 24% |
AIF-C01 asks which service fits a business problem. MLA-C02 asks how you would ingest and transform the data, tune the model, deploy the endpoint, wire the CI/CD pipeline and monitor it in production.
The out-of-scope lists are the clearest guide
AWS publishes what each candidate is not expected to do, and comparing them settles most decisions.
Out of scope for AIF-C01: coding models, data or feature engineering, hyperparameter tuning, building pipelines, statistical analysis, implementing security protocols, 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, working deeply in two or more ML domains.
Read those two lists back to back. Almost everything excluded from AIF-C01 is included in MLA-C02. That is the gap between them.
Which should you take?
Take AIF-C01 if you are a business analyst, product manager, consultant, solutions person, or a developer who consumes AI services rather than building models. Also take it if you are new to AI on AWS and want a structured map before going deeper. At 100 USD with no prerequisites, it is a low-risk entry point.
Take MLA-C02 if you already write code against SageMaker AI or Bedrock, prepare data for training, deploy endpoints, or operate ML in production. AWS expects a year of that experience. Without it, this exam will be painful regardless of how much you read.
Take both, in order, if you are moving from using AI to building it. AIF-C01 first is a genuinely useful foundation and it is cheap.
One thing AIF-C01 does that MLA-C02 does not
AIF-C01 uses four question types, including ordering and matching, both of which are all-or-nothing. MLA-C02 sticks to multiple choice and multiple response.
That makes AIF-C01’s format slightly more varied and slightly less forgiving per question, despite being the easier exam overall. Practise those formats specifically — knowing the content is not the same as getting an ordering question fully right under time pressure.