AI-300 vs AWS ML Engineer Associate: Which MLOps Cert?

Microsoft’s AI-300 and AWS’s Machine Learning Engineer – Associate (MLA-C01, now being replaced by MLA-C02) test the same job: getting machine learning models, and increasingly generative AI apps, into production and keeping them healthy. AI-300 does it on Azure Machine Learning and Microsoft Foundry. The AWS exam does it on Amazon SageMaker AI, with Amazon Bedrock playing a bigger role in the new version.

The short answer: take the one that matches the cloud you work in. If you don’t have a cloud yet, the differences below (cost, renewal, and how much generative AI each covers) will help you pick. All exam details are as of September 2026.

AI-300 vs MLA-C01 at a glance

AI-300 is English only. If English isn’t your first language, Microsoft’s study guide says you can request an extra 30 minutes when an exam isn’t offered in your preferred language. For the full AWS changeover, including which version to book, see our MLA-C02 exam guide.

What each exam covers

AI-300 skills measured

Add up the last three rows and generative AI operations are 40–55% of the exam. That’s the biggest thing to understand about AI-300: it isn’t a renamed DP-100. Alongside classic MLOps work (workspaces, MLflow tracking, training pipelines, endpoints, drift), you’ll deploy foundation models in Foundry, version prompts in Git, run quality and safety evaluations, track token costs, and tune RAG retrieval.

AWS ML Engineer – Associate domains

MLA-C01 is mostly classic ML engineering on SageMaker AI, with a heavy data-prep domain. MLA-C02 keeps the same four domains but adds generative AI throughout: vector databases, RAG, Bedrock knowledge bases, agents, guardrails, and LLM evaluation. So the two exams are converging, but AI-300 has the more explicit generative AI split today.

Same job, different tools

Both exam guides describe the same MLOps loop. Here’s how the named tools line up:

If you already know one side, the concepts carry over. What doesn’t carry over is the muscle memory: the console, the SDK, and the service names.

Which is harder?

It depends on where you’re coming from, because the two exams lean on different backgrounds.

• AI-300 leans DevOps. Microsoft expects GitHub Actions, command-line work, and Bicep templates on top of a data science background. If you’ve never written a pipeline YAML file or an IaC template, that’s your gap.
• AI-300 is also two platforms in one. You need Azure Machine Learning for classic models and Foundry for generative AI apps and agents.
• The AWS exam leans on hands-on SageMaker time. AWS recommends a year with SageMaker, and 28% of the exam is data preparation. AWS data services such as AWS Glue and Amazon Athena are on the in-scope list.
• Pace is hard to compare. MLA-C01 gives you 130 minutes for 65 questions. Microsoft doesn’t publish AI-300’s question count, so you can’t compare pace directly.

A data scientist moving into operations will probably find the AWS data-prep domain familiar and AI-300’s IaC work harder. A DevOps engineer moving into ML will likely find the reverse.

Cost and renewal over three years

The sticker prices are close. Microsoft prices AI-300 by the country where you test; AWS lists Associate exams in a handful of currencies. As of September 2026:

Taxes can apply on top. Check the price for your country on the AI-300 certification page and AWS’s pricing table before you budget.

Renewal is where they really differ. A Microsoft associate certification expires every year. Renewing is free: you take a short, open-book, unproctored assessment during a six-month window before expiry, and passing extends it a year. The catch is that it’s a yearly chore: miss the window and the certification expires. Microsoft’s renewal page walks through the steps.

The AWS credential lasts 3 years. To renew, you pass the latest exam version (and your 50% discount voucher makes that $75 at the US price), pass the Generative AI Developer – Professional exam, or keep it current through a paid AWS Skill Builder subscription, which adds one year at a time. Fewer deadlines, but a real exam at the end.

Which should you take? Stack-match advice

• Your company runs on Azure: AI-300. A certification in a cloud your team doesn’t use is a weak signal, however good it is.
• Your company runs on AWS: the ML Engineer – Associate. Check which version is open for your language before you book.
• You held DP-100: Microsoft named AI-300 as its replacement when it retired DP-100 on June 1, 2026, and a retired certification can’t be renewed. Our DP-100 retirement guide covers what to do with the old credential.
• Your work is mostly generative AI apps: AI-300 covers GenAIOps in detail today. On AWS, MLA-C02 is where that content lands.
• You don’t have a stack yet: search job postings in your area for “Azure Machine Learning” and “SageMaker” and count. Let the market near you decide, not the $15 price gap.
• You’re a consultant who works across clouds: taking both is reasonable. The shared concepts in the table above make the second exam faster to prepare for.

Still on the fence about Microsoft’s exam itself? Read our honest look at whether AI-300 is worth it.

If you know one cloud and want to size up the other, this prompt builds a translation sheet from the official skills list:

You are an MLOps engineer who has worked on both Microsoft Azure and AWS.I know: [Azure Machine Learning and Foundry / Amazon SageMaker AI and Bedrock]I'm considering: [AI-300 / the AWS Certified Machine Learning Engineer - Associate]My role and experience: [describe]Below is the official skills list for the exam I'm considering. For each skill:1. Name the closest equivalent in the cloud I already know, or write "no direct equivalent."2. Rate my likely readiness (ready / needs review / new to me) based on my experience.3. Give one hands-on exercise I could do in under an hour to close the gap.Then summarize my top 5 gaps and estimate whether I should study for this exam or stick with my current cloud's cert.Rules: only use services and features that appear in the list or that you are certain exist. If you are unsure whether a feature exists, say so instead of guessing.[PASTE THE OFFICIAL SKILLS LIST HERE]

You are an MLOps engineer who has worked on both Microsoft Azure and AWS.I know: [Azure Machine Learning and Foundry / Amazon SageMaker AI and Bedrock]I'm considering: [AI-300 / the AWS Certified Machine Learning Engineer - Associate]My role and experience: [describe]Below is the official skills list for the exam I'm considering. For each skill:1. Name the closest equivalent in the cloud I already know, or write "no direct equivalent."2. Rate my likely readiness (ready / needs review / new to me) based on my experience.3. Give one hands-on exercise I could do in under an hour to close the gap.Then summarize my top 5 gaps and estimate whether I should study for this exam or stick with my current cloud's cert.Rules: only use services and features that appear in the list or that you are certain exist. If you are unsure whether a feature exists, say so instead of guessing.[PASTE THE OFFICIAL SKILLS LIST HERE]

Grab the skills list from the AI-300 study guide or AWS’s MLA exam guide. For AWS practice, try our free AWS ML Engineer practice questions, which follow the MLA-C01 blueprint.

Common mistakes

• Choosing on price. A $15 difference is noise next to the hours you’ll spend studying a platform you don’t use.
• Studying old DP-100 material for AI-300. Generative AI operations make up 40–55% of AI-300. Use the current study guide.
• Forgetting AI-300 expires yearly. Put a calendar reminder about six months after you pass, when the renewal window opens.
• Planning around English MLA-C01 after September 28, 2026. That’s its last English date. After it, English candidates choose the MLA-C02 beta or wait for the standard version, which AWS’s certification blog dates to January 14, 2027 (the exam page still says TBD).
• Treating the AWS beta like a normal attempt. AWS allows one beta attempt; a fail means waiting for the standard exam.

Try it now

Open the official skills list for the exam that matches your employer’s cloud and highlight every line you couldn’t do at work tomorrow. That list is your study plan. If it’s longer than half the page, give yourself more than a month. Then take a free practice set on our AI exam prep hub to get a baseline score.

Related guides

• Microsoft AI certifications in 2026: the new path, explained
• Is the AWS ML Engineer Associate worth it?
• Is the AI-300 Certification Worth It? An Honest Look at Microsoft’s MLOps Cert
• AWS ML Engineer Associate (MLA-C01) Study Guide & Cheat Sheet
• DP-100 Is Retired: What Replaced Azure’s Data Science Certification

HOW TO // AI is not affiliated with or endorsed by Microsoft or Amazon Web Services. Exam details come from Microsoft Learn and AWS certification pages as of September 2026 and can change. Check the official AWS exam page and Microsoft’s AI-300 page before you book.