AI-901 vs AWS AIF-C01: which cloud AI certification should you start with?

If you're choosing your first cloud AI certification, you've probably come across the two most popular foundational options: AI-901 from Microsoft Azure and AIF-C01 from AWS. Both validate that you understand AI concepts and each cloud's managed services without requiring you to train a model from scratch — but they're not interchangeable, and since 2026 they're less alike than they seem. Here's the direct comparison, no filler.

What each one covers

AI-901 (Microsoft Azure AI Fundamentals) replaced the AI-900 on April 15, 2026, and changed its approach: it has only two domains. The first (40-45%) covers AI concepts and capabilities — responsible AI, how generative models work, and text, speech, vision, and information extraction workloads. The second (55-60%) is hands-on: implementing solutions with Microsoft Foundry — prompts, model deployment, agents, and lightweight apps with the SDK, along with services like Azure Speech and Foundry Tools (formerly Cognitive Services). Azure OpenAI Service is still the gateway to generative models within Azure.

AIF-C01 (AWS Certified AI Practitioner) covers AI/ML fundamentals, generative AI, use cases and applications, and — unlike AI-901 — dedicates a full domain to security, governance, and compliance for AI systems. The flagship service here is Amazon Bedrock, along with concepts like Bedrock Guardrails for controlling a generative model's outputs in production.

Conceptually, both exams touch the same pillars: machine learning, deep learning, NLP, computer vision, and the fundamentals of a language model. If you've already studied for one, a good part of the theory carries over; what changes are each provider's services and, in the AI-901, the weight of the hands-on part.

Difficulty: which one is harder?

Neither asks you to train models, but they're no longer equally conceptual. Here's where they differ:

• ▸AI-901 is more hands-on than its name suggests: more than half of the exam is about implementing with Microsoft Foundry — deploying a model, writing effective prompts, building an agent or a lightweight app with the SDK. Recognizing which service solves which problem isn't enough; you need to know how it's used.
• ▸AIF-C01 is still scenario-based with no code, but with a lot of applied reasoning: deciding between tweaking a prompt, fine-tuning, or using RAG to solve a business case, plus a full domain on security and governance.

Both are passable in 2-3 weeks of study if you put in a couple of hours a day, especially if you use an exam simulator to practice the real question format before taking the test.

Who each one is for

The short answer: it depends on which cloud your company uses (or will use).

Choose AI-901 if:

• ▸Your company uses Microsoft 365, Azure, or Power Platform as its main stack.
• ▸You're looking for a bridge toward more advanced data or AI certifications within the Microsoft ecosystem (Azure Data Scientist Associate, for example).
• ▸You're interested in working with teams that already integrate Copilot or Azure OpenAI Service into their products.

Choose AIF-C01 if:

• ▸Your company runs its infrastructure on AWS or is evaluating Bedrock for generative AI.
• ▸You want a certification with more weight on governance and responsible AI, useful if your role has a compliance or risk component.
• ▸You plan to continue toward AWS Certified Machine Learning Engineer or other AWS ML certifications.

If you don't have a defined cloud yet (for example, you're job hunting and don't know which stack your next employer will use), look at job postings in your local market before deciding. As a general rule, AWS tends to have more presence in startups and AWS-native shops, while Azure dominates in companies that are already Microsoft customers (banks, government, traditional corporations).

Does it make sense to do both?

Yes, and it's more common than it seems. Since the conceptual content overlaps quite a bit — transformer architecture, embeddings, and foundation models appear in both study programs — the second certification takes a fraction of the time of the first. Many candidates take the one for their main cloud first and then the other within a month, taking advantage of having the generative AI vocabulary fresh.

This makes sense especially if:

• ▸You work in consulting or freelance and need to talk to clients on both clouds.
• ▸Your company is in the process of evaluating multi-cloud.
• ▸You want to strengthen your AI profile without committing yet to a deeper specialization (that would come later, with prompt injection and LLM security as the next step up in knowledge).

If your goal is just "having an AI certification on your résumé" and you don't plan to actively use either cloud, pick the one with more presence in the job postings you're seeing — there's no reason to pay for the second if you won't apply it.

How to prepare

For both certifications, the strategy that works best is the same: understand the "why" behind each service (not just memorize names), and then do enough practice questions to get used to the question style — which tends to be more scenario-based than pure definition. You can check out the AI-901 cheat sheet, the AI-901 study guide, and the AIF-C01 guide on Brains4Cloud to organize your study plan, and complement them with the full glossary to review terms that show up in both exams.

In the end, neither certification will get you a job on its own — but they do give you enough vocabulary and context to take part in technical conversations about AI without staying silent. Choose the one for your main cloud, and if you have the time, the second one is a cheap investment.