AWS MLA-C01 vs AIP-C01: Which AI Certification Should You Take First?

AWS now offers two AI-focused certifications, and the naming can be confusing. The AI Practitioner (AIP-C01) and the Machine Learning Engineer Associate (MLA-C01) sound like they belong on the same track, and they do — but they target very different audiences and test very different skills.

If you are trying to decide which one to pursue first, this comparison will give you a clear answer in about five minutes.

The Quick Comparison

Factor	AIP-C01 (AI Practitioner)	MLA-C01 (ML Engineer)
Level	Foundational	Associate
Target role	Anyone working with/around AI	ML engineers, data scientists
Coding required	No	Yes (Python, ML libraries)
Hands-on experience	Helpful, not required	1+ year with SageMaker
Key focus	AI concepts, generative AI, Bedrock	ML pipelines, SageMaker, MLOps
Questions	85	85
Time	120 minutes	170 minutes
Passing score	700/1000	720/1000
Exam cost	$100	$150
Study time	4-6 weeks	8-12 weeks
Difficulty	5/10	7/10

Who Should Take AIP-C01 First

The AI Practitioner is designed for people who work with AI but do not necessarily build ML models. This includes:

• Product managers who need to understand AI capabilities and limitations to make product decisions
• Business analysts who evaluate AI solutions for their organization
• Software developers who use AI services (Bedrock, Comprehend, Rekognition) but do not build custom models
• Consultants and architects who advise clients on AI strategy
• Technical managers who lead teams working on AI projects
• Career changers entering the AI space who want a credential while building hands-on skills

The exam tests your understanding of AI and ML concepts, generative AI, prompt engineering, responsible AI, and how to choose the right AWS AI service for a given problem. You do not need to write code, build pipelines, or configure SageMaker training jobs.

For a full study guide, see our AIP-C01 exam preparation guide.

Who Should Take MLA-C01 First

The ML Engineer certification is for people who build ML systems. This includes:

• 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 transitioning into ML engineering roles
• Anyone with 1+ year of hands-on SageMaker experience

The exam tests data engineering for ML (Glue, EMR, Feature Store), model training and evaluation (SageMaker algorithms, hyperparameter tuning), deployment (endpoint types, blue/green, canary), and monitoring (Model Monitor, data drift, retraining).

If you cannot describe the difference between SageMaker real-time inference, serverless inference, batch transform, and async inference — and when to use each — you are not ready for this exam.

For a full study guide, see our MLA-C01 exam preparation guide.

The Optimal Order

Scenario 1: You Are New to AWS AI

Take AIP-C01 first. It builds the conceptual foundation you need before diving into engineering specifics. You will learn what foundation models are, how Bedrock works, what responsible AI means, and how AWS AI services map to real problems.

Then, build hands-on experience with SageMaker for 3-6 months. After that, tackle MLA-C01 with both conceptual understanding and practical skills.

Timeline: AIP-C01 now, MLA-C01 in 4-6 months.

Scenario 2: You Already Have ML Engineering Experience

You can skip AIP-C01 and go straight to MLA-C01 if you have:

• 1+ year of hands-on experience with SageMaker
• Strong Python and ML library skills (scikit-learn, PyTorch, TensorFlow)
• Experience building data pipelines with Glue or EMR
• Understanding of MLOps concepts

The AIP-C01 content will feel too basic for you. Your time is better spent on the more challenging and more career-relevant MLA-C01.

Timeline: MLA-C01 now.

Scenario 3: You Have Cloud Experience but Are New to ML

Take AIP-C01 first. Your cloud knowledge will help with the security and governance domains, and the exam will teach you the AI/ML concepts you are missing. Use the preparation time to also start hands-on experimentation with SageMaker.

Timeline: AIP-C01 now, start SageMaker labs in parallel, MLA-C01 in 6-8 months.

Scenario 4: You Want Both Certifications

Start with AIP-C01. The generative AI and responsible AI knowledge transfers directly to MLA-C01 preparation. You will not waste time — the AIP-C01 study material gives you a head start on roughly 25% of the MLA-C01 content.

Timeline: AIP-C01 in month 1-2, MLA-C01 in month 4-5.

Salary and Career Impact

Both certifications boost your career, but in different ways:

• AIP-C01 broadens your profile. It signals AI literacy and is valuable for roles that involve AI strategy, solution design, or cross-functional AI work. Salary premium: 8-12%.
• MLA-C01 deepens your profile. It validates engineering skills that are in high demand and short supply. Salary premium: 15-22%.

If you hold both, the combination signals that you understand AI at both the strategic and engineering levels — a rare and valuable profile.

The Decision in One Sentence

If your job requires you to build ML models and pipelines on AWS, take MLA-C01. If your job requires you to understand, evaluate, or work with AI but not build models, take AIP-C01.

When in doubt, start with AIP-C01. It is faster, cheaper, and builds the foundation for everything that comes after. Open StudyKits and start your first practice set today.