The AWS Certified AI Practitioner (AIF-C01) is a foundational exam that proves you understand AI, ML, and generative AI concepts on AWS. The AWS Certified Machine Learning Engineer Associate (MLA-C02) is an associate-level exam that proves you can build, deploy, and operate ML and AI solutions. Neither is a prerequisite for the other.
Last updated September 2026.
| AIF-C01 | MLA-C02 | |
|---|---|---|
| Level | Foundational | Associate |
| Cost | $100 USD | $150 USD standard (beta priced lower) |
| Questions | 65 (50 scored, 15 unscored) | 65 (50 scored, 15 unscored) |
| Time limit | 90 minutes | 130 minutes |
| Passing score | 700 (100 to 1,000 scale) | 720 (100 to 1,000 scale) |
| Question types | Multiple choice, multiple response, ordering, matching, case study | Multiple choice, multiple response |
| Target candidate | Up to 6 months of exposure to AI and ML on AWS | 1 year with SageMaker AI and Bedrock, plus 1 year in a related role |
| Domains | 5 (generative AI is 52%) | 4 (28/24/24/24) |
| Validity | 3 years | 3 years |
From the official AIF-C01 exam guide and the official MLA-C02 exam guide. MLA-C01, the previous version, is offered in English until September 28, 2026.
The clearest way to separate the two is the verb each exam uses. AIF-C01 asks you to describe, identify, and choose: what a foundation model is, when fine-tuning beats retrieval-augmented generation, which AWS service fits a use case, and how responsible AI and governance apply. Its five domains are Fundamentals of AI and ML (20%), Fundamentals of Generative AI (24%), Applications of Foundation Models (28%), Guidelines for Responsible AI (14%), and Security, Compliance, and Governance for AI Solutions (14%).
MLA-C02 asks you to build, deploy, and operate. Its domains are Data Preparation for ML and AI (28%), ML and Foundation Model Development (24%), Deployment and Orchestration (24%), and Operating, Monitoring, and Securing (24%). You pick inference infrastructure, tune hyperparameters, wire SageMaker Pipelines into CI/CD, monitor drift, and configure Bedrock Guardrails and IAM. A question that AIF-C01 answers with a service name, MLA-C02 answers with an architecture.
The overlap is real but shallow: Amazon Bedrock, RAG, prompt engineering, and fine-tuning appear on both. On AIF-C01 you recognise them; on MLA-C02 you implement, evaluate, and secure them.
You are in a product, business, sales, or non-ML technical role, you are new to AI on AWS, or you want a low-cost first credential ($100, 90 minutes) before committing to an engineering exam.
You already train or deploy models, write Python, and use SageMaker AI or Bedrock at work. AIF-C01 would mostly confirm what you know, and your study time is better spent on the associate exam.
Taking both in sequence is reasonable if AI is new to you: AIF-C01 builds the generative AI vocabulary that now runs through every MLA-C02 domain. But it is a ramp, not a requirement. If you are an engineer with a year of SageMaker AI experience, skip the ramp.
No. AWS has no prerequisites for either exam, so you can sit the Machine Learning Engineer Associate directly. AIF-C01 is a useful ramp if AI is new to you, but experienced ML engineers usually skip it.
Yes. AIF-C01 is a foundational exam that tests concepts in 90 minutes with a 700 passing score, while MLA-C02 is an associate exam that tests hands-on engineering in 130 minutes with a 720 passing score. MLA-C02 expects about a year of SageMaker AI and Bedrock experience.
Yes, from MLA-C02 onward. MLA-C02 adds foundation models, RAG, agents, and Bedrock Guardrails to all four domains, tested at implementation depth rather than the concept level AIF-C01 uses.
AIF-C01 costs $100 USD and the Machine Learning Engineer Associate costs $150 USD at the standard associate fee, with the MLA-C02 beta offered at a reduced price. Prices vary by country and tax may be added.
Certified's MLA-C02 course covers all four domains in 76 short chapters, with unit practice exams and three full-length mocks.
Open the MLA-C02 course →