Are the AWS, Google and Microsoft AI certifications equivalent?

Roughly, in level — all are foundational, with no coding or maths prerequisite, and none certifies engineering ability. They differ in emphasis: AWS is broad AI/ML plus AWS services, Google is generative-AI strategy for leaders, and Microsoft is broad AI concepts on Azure. Their exam mechanics also differ, from scoring to validity.

Do these AI certifications require coding?

No. All three are foundational, conceptual exams with no programming, mathematics or hands-on prerequisite. They test fluency — what AI and generative AI are, how they fail, responsible use, and the vendor’s services — through multiple-choice questions. Building AI systems requires separate technical study that none of these exams covers or claims to.

What are the passing scores for the foundational AI certifications?

AWS scores the AI Practitioner on a 100–1000 scale with a 700 pass mark; Microsoft’s AI-900 pass mark is 700 out of 1000; Google publishes no numeric score — its results are simply pass or fail. All three are delivered through Pearson VUE. Fees change, so check each vendor’s official certification page for current details.

Should I take more than one foundational AI certification?

Usually not — the conceptual core overlaps heavily, so a second foundational badge adds little beyond the vendor-services layer. One fluency credential plus visible hands-on work is a stronger signal than a collection of foundations. Take a second only with a concrete reason, such as changing employers to a different cloud platform.

AI & ML

Generative AI certifications compared: AWS, Google and Microsoft

Updated 27 Jul 2026

The main foundational generative-AI and AI certifications from the big cloud providers — the AWS Certified AI Practitioner, Google Cloud’s Generative AI Leader, and Microsoft’s Azure AI Fundamentals — all validate AI fluency rather than engineering skill, and the right choice depends on which cloud you use and whether you want a broad-AI or GenAI-strategy focus. All three are foundational: none assumes coding, mathematics or hands-on experience, and none will make you a machine-learning engineer. What they offer instead is a structured syllabus and a verifiable, vendor-backed signal that your AI knowledge is organised rather than absorbed piecemeal from headlines. This comparison describes each credential accurately, sets them side by side, explains how to choose, and is honest about the shared limit — and about the surprisingly different exam mechanics behind each vendor’s badge.

The three credentials, described fairly

The AWS Certified AI Practitioner (AIF-C01) is the broadest of the three. It covers AI and machine-learning fundamentals, generative AI, responsible AI, security and governance, and a substantial layer of AWS’s own AI services — how the platform’s managed offerings map to problem types. It suits people who want general AI/ML literacy anchored to the most widely used cloud.

Google Cloud’s Generative AI Leader is the most explicitly business-oriented. Its domains centre on generative-AI fundamentals, techniques for improving model output such as prompting and grounding, and business strategies for successful adoption, alongside responsible and operational considerations. It reads as a credential for decision-makers first. Microsoft’s Azure AI Fundamentals (AI-900) sits closer to the AWS exam in shape: broad AI and machine-learning concepts — including generative AI — expressed through Azure’s services and tooling. All three are foundational with no formal prerequisite, and our individual guides to the AWS AI Practitioner and the Google Generative AI Leader cover each in depth.

Side by side

The essential differences compress into a short list:

AWS Certified AI Practitioner (AIF-C01) — vendor: AWS; focus: broad AI/ML plus generative AI, responsible AI and AWS AI services; audience: anyone wanting general AI fluency on the AWS platform; level: foundational.
Google Cloud Generative AI Leader — vendor: Google Cloud; focus: generative-AI strategy, output-improvement techniques and business adoption; audience: leaders, decision-makers and business professionals; level: foundational.
Microsoft Azure AI Fundamentals (AI-900) — vendor: Microsoft; focus: broad AI/ML concepts, including generative AI, on Azure; audience: anyone wanting AI fluency in a Microsoft-centred environment; level: foundational.

How to choose

The first filter is the cloud you use or target. These are vendor credentials, and a meaningful share of each syllabus is the vendor’s own services — so a certification aligned with your organisation’s platform, or the platform named in the job adverts you care about, is worth more in practice than a marginally “better” one on a cloud you never touch. If your employer runs on Azure, AI-900 converts directly into workplace vocabulary; the same logic applies to AWS and Google Cloud.

The second filter is framing. If your role is genuinely strategic — approving initiatives, scoping products, advising organisations — Google’s Generative AI Leader matches that job most directly. If you want the widest conceptual grounding across AI and machine learning, the AWS and Microsoft exams cover more territory beyond generative AI. And if you are cloud-agnostic, it is reasonable to choose by employer demand in your market: read live job adverts rather than certification marketing.

The honest shared limit

None of these certifications makes you an AI or machine-learning engineer, and all three vendors are upfront about that. They are multiple-choice evidence of fluency: that you understand what these systems are, how they fail, how to use them responsibly and what the vendor’s services do. The path to building AI systems runs through programming, mathematics and real projects — territory our machine learning engineer career path maps in detail — and no foundational exam substitutes for it.

That limit is not a reason to skip them; it is a reason to hold them to their honest job. Paired with visible hands-on work — even small projects — a foundational AI credential opens conversations and clears screens. Presented as engineering capability, it invites the interview question that deflates it. Our guide on whether AI certifications are worth it makes the full case, including why we publish no salary or demand statistics: no honest ones exist at the precision the marketing implies, and your own evidence is the live job market.

Exam mechanics: three vendors, three rulebooks

A practical trap when comparing across vendors is assuming they all run exams the same way — they do not. AWS scores the AI Practitioner on a scaled 100–1000 range with a pass mark of 700, delivers it through Pearson VUE, and the certification is valid for three years. Microsoft also uses Pearson VUE with a pass mark of 700 out of 1000; notably, Azure Fundamentals certifications such as AI-900 do not expire. Google delivers its exams through Pearson VUE too — it moved there in early 2026 — but publishes no numeric passing score at all: results are simply pass or fail.

Fees vary by certification and can change, so check each vendor’s official certification page for current pricing rather than relying on third-party figures. The mechanics matter less than they appear — every one of these is a proctored multiple-choice exam a well-prepared candidate passes — but knowing the rulebook you are actually playing under removes a layer of avoidable anxiety.

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AIF-C01 AZ-900 GenAI Leader

Questions, answered

There is no single best — all three foundational options (AWS AI Practitioner, Google Cloud Generative AI Leader, Microsoft Azure AI Fundamentals) validate AI fluency rather than engineering skill. Choose by the cloud your organisation or target roles use, then by framing: Google’s leans toward business strategy, while AWS’s and Microsoft’s cover broader AI/ML ground on their platforms.