MLA-C01 vs AIF-C01: Which AWS AI Certification First?

AWS MLA-C01 vs AI Practitioner (AIF-C01): Which AI Certification Should You Take First? — hero

Take AI Practitioner (AIF-C01) first if you work with AI but do not build models. Take Machine Learning Engineer - Associate first if you already train and deploy models on AWS. AIF-C01 is foundational (90 minutes, USD 100); the ML Engineer exam is associate level (130 minutes, USD 150).

Practice this exam in timed mode with the StudyKits AIP-C01 Generative AI Developer Professional app. The AWS certification hub lists every AWS exam guide and practice app.

One timing point matters in late 2026. MLA-C01 in English ended on September 28, 2026; English candidates now meet MLA-C02, in beta. AWS’s exam page lists MLA-C02 general availability as TBD; AWS’s September 2026 announcement gives January 14, 2027 for general availability. Exam facts below come from AWS pages listed under Sources, checked October 15, 2026.

Naming note: the foundational AWS AI Practitioner exam is AIF-C01. AIP-C01 is a different, professional exam (AWS Certified Generative AI Developer - Professional). This page’s URL contains “aip-c01” for link stability; the comparison is MLA vs AIF-C01.

How do AIF-C01 and the ML Engineer exam compare?

Factor	AIF-C01 (AI Practitioner)	MLA-C01 (ML Engineer)	MLA-C02 (ML Engineer, GA announced Jan 14, 2027)
Level	Foundational	Associate	Associate
Target role	People who use AI/ML on AWS	ML engineers, data scientists	ML engineers, data scientists
Builds models?	No	Yes	Yes, plus foundation models
Questions	65 (50 scored + 15 unscored)	65	65 (50 scored + 15 unscored); beta 85
Time	90 minutes	130 minutes	Beta 170 minutes
Passing score	700 (scaled 100-1,000)	720	720 (not for the beta)
Exam cost	USD 100	USD 150	Beta USD 75
Status (Oct 2026)	Current	Japanese, Korean, Simplified Chinese until Jan 14, 2027	Beta in English since Sep 29, 2026

Who should take AI Practitioner (AIF-C01) first?

AWS describes the AIF-C01 candidate as someone who uses AI/ML on AWS but does not necessarily build it. That usually means:

Product managers who need to know what AI can and cannot do
Business analysts who evaluate AI solutions
Software developers who call AI services (Bedrock, Comprehend, Rekognition) but do not train models
Consultants and architects who advise on AI strategy
Career changers who want a first credential while building hands-on skills

The exam covers AI and ML basics, generative AI, applications of foundation models, responsible AI, and security and governance for AI. You do not write code or configure SageMaker training jobs.

To compare AIF-C01 with Azure AI Fundamentals and Google’s PMLE, see AWS AI Practitioner vs Azure AI Fundamentals vs Google PMLE.

Who should take the ML Engineer exam first?

The ML Engineer - Associate exam is for people who build ML systems:

Machine learning engineers who train and deploy models on AWS
Data scientists who want to formalize their AWS engineering skills
MLOps engineers who build and maintain ML pipelines
Backend engineers moving into ML engineering with real SageMaker practice

MLA-C01 has four domains: data preparation (28%), model development (26%), deployment and orchestration (22%), and monitoring, maintenance and security (24%).

A quick readiness check: can you explain SageMaker real-time, serverless, batch and asynchronous inference, and when to use each? If not, build more hands-on time first.

For the full plan, see the MLA-C01 exam preparation guide.

What changes with MLA-C02?

MLA-C02 keeps four domains but adds AI to each: data preparation for ML and AI (28%), ML and foundation model development (24%), deployment and orchestration of ML and AI workflows (24%), and operating, monitoring and securing ML and AI solutions (24%).

New topics include generative and agentic AI, Bedrock knowledge bases and Guardrails, RAG, vector databases and LLM evaluation. SageMaker Neo, bring-your-own-container and model-size reduction are removed. That brings MLA-C02 closer to AIF-C01’s generative AI content, so AIF-C01 study carries over more than before.

Which order fits your situation?

You are new to AWS AI

Take AIF-C01 first. It gives you the vocabulary for foundation models, Bedrock and responsible AI. Then build hands-on SageMaker experience before the ML Engineer exam.

You already build ML systems

Go straight to the ML Engineer exam. Plan for MLA-C02 if you sit in English, or MLA-C01 in Japanese, Korean or Simplified Chinese before January 14, 2027.

You have cloud experience but are new to ML

Take AIF-C01 first. Your cloud knowledge helps with the security and governance domain. Start SageMaker labs in parallel.

You want both

Start with AIF-C01. Its generative AI and responsible AI content overlaps with MLA-C02’s new topics.

How should you practise for each exam?

Use AWS Skill Builder for official training and practice sets for both exams. Practise with timed sets so the 90- or 130-minute limit feels normal before exam day.

StudyKits has no AIF-C01 app. The StudyKits ML Engineer app covers MLA-C01, not MLA-C02.

Frequently asked questions

Is AIF-C01 the same as AIP-C01?

No. AIF-C01 is AWS Certified AI Practitioner (foundational, USD 100). AIP-C01 is AWS Certified Generative AI Developer - Professional (180 minutes, USD 300).

Can I still take MLA-C01?

In English, no: the last English day was September 28, 2026. Japanese, Korean and Simplified Chinese delivery continues until January 14, 2027.

Do I need AIF-C01 before the ML Engineer exam?

No. AWS does not require it. It helps if you are new to AI concepts.

Which is harder?

The ML Engineer exam. It is associate level, longer, has a higher passing score (720 vs 700) and expects hands-on model building.

Keep reading

AIP-C01 Study Guide: AWS Certified Generative AI Developer

Sources

Checked October 15, 2026:

AWS MLA-C01 vs AI Practitioner (AIF-C01): Which AI Certification Should You Take First?