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Quick answer
Ranked from easiest to hardest, in our judgment: the two foundational exams, AWS AI Practitioner (AIF-C01) and Azure AI Fundamentals, whose exam is now AI-901 (it replaced AI-900 on 30 June 2026), then NVIDIA NCA-GENL, Microsoft's AI-103 (which replaced AI-102), AWS ML Engineer Associate (MLA-C01 / MLA-C02), NVIDIA's NCP professional tracks and, hardest of all, the Google Cloud Professional ML Engineer. The easiest proctored AI credential that needs no code is now the AWS AI Practitioner, since AI-901 expects basic Python. For that first rung, Udemy’s Ultimate AWS Certified AI Practitioner AIF-C01 is a taught prep course of about 10 hours, with no coding. Difficulty is not value: the right exam matches your stack and background.
AI-102 is retired. The replacement is Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103).
Exam AI-900: Microsoft Azure AI Fundamentals is retired. The replacement is Exam AI-901: Microsoft Azure AI Fundamentals, which earns the same Azure AI Fundamentals certification but expects Python and familiarity with REST APIs and SDKs.
See Ultimate AWS Certified AI Practitioner AIF-C01 on Udemy →
The table below compares 7 certifications on provider, level, realistic time, coding needed and best for.
| Certification | Provider | Level | Realistic time | Coding needed | Best for | Enrol |
|---|---|---|---|---|---|---|
| AWS Certified AI Practitioner (AIF-C01) | AWS | Foundational | ~4–6 weeks of prep | No | Rung 1 (easiest): no code, broad scope, scenario wording | AWS → |
| Azure AI Fundamentals (AI-901) | Microsoft | Foundational | Varies with Python experience | Basic Python | Rung 2: concepts plus small Foundry builds (replaced AI-900 on 30 June 2026) | |
| NVIDIA NCA-GENL | NVIDIA | Associate | ~4–8 weeks of prep | Some (Python helps) | Rung 3: LLM fundamentals with light technical depth | |
| Azure AI Apps and Agents Developer Associate (AI-103) | Microsoft | Intermediate (associate) | ~6 weeks for working devs | Yes (Python) | Rung 4: first exam that assumes you already build apps (replaced AI-102) | Microsoft → |
| AWS ML Engineer Associate (MLA-C01 / MLA-C02) | AWS | Intermediate (associate) | ~2–3 months of prep | Yes (Python) | Rung 5: full ML lifecycle operations | |
| NVIDIA NCP professional tracks | NVIDIA | Professional | ~2–3 months of prep | Yes | Rung 6: infrastructure depth, experience assumed | |
| Google Cloud Professional ML Engineer | Google Cloud | Advanced (professional) | ~3 months for experienced engineers | Yes (Python) | Rung 7 (hardest): scenario judgment across the ML lifecycle |
What makes an AI certification exam hard?
Four factors separate the rungs, and knowing them tells you what you are actually signing up for. Scope: how many services and concepts the outline covers. Hands-on assumptions: whether questions expect you to have built things, not just read about them. Scenario density: recall questions ('what is X?') versus judgment questions ('given these constraints, which approach?'). And format: multiple choice is forgiving; case studies and multi-step scenarios are not.
The easy band leans on recall with generous formats. The hard band is almost entirely judgment under constraint — which is why experience, not study hours, is what separates candidates at the top of the ladder.
What each vendor says you need before you sit
Vendors publish no pass rates, but each one says who its exam is written for, and those statements line up with this ladder almost exactly. The table quotes each vendor's own exam guide or exam page, read on 16 October 2026.
The table below lists the seven exams on our ladder, easiest first, with the experience each vendor says a candidate should bring.
| Rung | Exam | What the vendor says you should bring | Enrol |
|---|---|---|---|
| 1 | AWS Certified AI Practitioner (AIF-C01) | Up to six months of exposure to AI and ML on AWS; AWS writes it for someone who “uses but does not necessarily build” AI solutions | AWS → |
| 2 | Azure AI Fundamentals (AI-901) | Someone “at the beginning of your career in AI solution development”, with knowledge of Python coding syntax and familiarity with Azure resources | |
| 3 | NVIDIA NCA-GENL | “A basic understanding of generative AI and large language models” | |
| 4 | Azure AI Apps and Agents Developer Associate (AI-103) | Experience developing apps in Python, and familiarity with generative AI and Azure services | Microsoft → |
| 5 | AWS Certified Machine Learning Engineer – Associate (MLA-C02) | At least a year using Amazon SageMaker AI, Amazon Bedrock and other AWS services for ML engineering, plus at least a year in a related role such as backend developer, data engineer or data scientist | AWS → |
| 6 | NVIDIA NCP-GENL, the professional generative-AI exam | Two to three years of practical experience in AI or ML roles working with large language models | |
| 7 | Google Cloud Professional Machine Learning Engineer | Three or more years of industry experience, including at least one year designing and managing solutions on Google Cloud |
The line between the bands is visible in the vendors' own words: below rung 5, no vendor asks for a set amount of experience; from rung 5 up, every one asks for at least a year of the work the exam certifies. The fee and format of all seven, in each vendor's own figures, are in the next table.
The table below shows the fee and format of the seven exams this page discusses, as each vendor publishes them. Every other exam we track is compared on our AI certification exams page.
| Credential | Vendor | Level | Fee | Exam format | Enrol |
|---|---|---|---|---|---|
| AWS Certified AI Practitioner (AIF-C01) | AWS | Foundational | 100 USD | 90 minutes · 65 questions | AWS → |
| Microsoft Certified: Azure AI Fundamentals (AI-901) | Microsoft | Foundational | $99 USD (varies by country/region) | 45 minutes | Microsoft → |
| NVIDIA-Certified Associate Generative AI LLMs | NVIDIA | Beginner | $125 | 60 minutes · 50–60 questions · valid 2 years | NVIDIA → |
| Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103) | Microsoft | Intermediate | $165 USD (varies by country/region) | 120 minutes | Microsoft → |
| AWS Certified Machine Learning Engineer – Associate (MLA-C01 / MLA-C02) AWS set 28 September 2026 as the last day to take MLA-C01 in English; MLA-C02 replaces it. | AWS | Intermediate | $150 USD (MLA-C01); $75 USD beta pricing for MLA-C02 | MLA-C01: 130 minutes · 65 questions; MLA-C02 beta: 170 minutes · 85 questions | AWS → |
| NVIDIA-Certified Professional Generative AI LLMs | NVIDIA | Intermediate | $200 | 120 minutes · 60–70 questions · valid 2 years | NVIDIA → |
| Professional Machine Learning Engineer | Google Cloud | Advanced | $200 (plus tax where applicable) | 2 hours · 50–60 questions | Google Cloud → |
Not sure this is the right one for you?
Tell the picker about your background and what you want the certificate to do, and it narrows the list to the one or two courses we would start with. It suggests only our affiliate partners’ courses, and says so before it suggests anything.
Try the AI Certification Picker →The easy band: AWS AI Practitioner and AI-901
Both are foundational and designed for broad audiences — business roles included — but only one is still no-code. The AWS AI Practitioner puts coding out of scope; it covers a lot of ground — generative AI and foundation-model applications get real weight — and its scenario wording trips up people who skimmed; the AIF-C01 study guide covers the plan. Azure AI Fundamentals used to be the gentler of the two, but AI-900 was retired on 30 June 2026 and its replacement, AI-901, expects basic Python and hands-on work in Microsoft Foundry — our AI-900 study guide explains what changed. Neither should intimidate anyone willing to do structured prep, and both clear the credibility bar with employers — see our ranking of the easiest certifications employers respect.
If you want a taught course for the first rung rather than AWS’s free training alone, the one we would use is Udemy’s Ultimate AWS Certified AI Practitioner AIF-C01: about 10 hours, with no coding, on Amazon Bedrock, prompt engineering, SageMaker, AWS’s managed AI services and AI’s challenges and responsibilities, with one practice exam included. It is bought once on Udemy and ends in a certificate of completion; the certification itself comes only from passing AWS’s proctored exam.
The middle band: NCA-GENL and AI-103
This is where prerequisites begin. NVIDIA's NCA-GENL is still an associate exam, but its LLM content rewards candidates who have actually worked with models rather than read summaries. Microsoft's AI-103, which replaced the retired AI-102, is the bigger step: it assumes experience developing apps in Python and real Azure hours, and its scenarios mirror implementation decisions — our guide to AI-102's retirement and its replacement is blunt that sandbox time matters more than videos. Treat this band as 'practitioner entry': passable without years of experience, not passable without hands-on practice.
The hard band: AWS ML Engineer Associate, NCP and the GCP ML Engineer
The top three rungs are engineering exams. The AWS ML Engineer Associate covers the full ML lifecycle in production — data preparation through monitoring — and rewards operational fluency over theory. NVIDIA's NCP professional tracks assume genuine infrastructure experience with GPU systems. And the Google Cloud Professional ML Engineer sits at the top of our ladder: professional-tier from the first question, scenario judgment throughout, and no associate stepping stone beneath it. None of these is a study-only exam; all three assume you have shipped something.
How do you pick the right rung?
Start from your background and your stack, not from the ladder. Three rules cover most cases:
- No technical background: start in the easy band on whichever cloud your employer runs — on Azure, allow for AI-901's basic Python — and treat it as a vocabulary credential; nothing above rung 3 makes sense without code.
- Working developer or data engineer: skip the foundational rungs entirely and enter at AI-103 or the AWS ML Engineer Associate on your platform — the lower exams add little once you can build.
- Experienced ML practitioner: the GCP ML Engineer or an NCP track is the only band that will teach you anything — but take it when your work matches, not for the flex.
Whatever the rung, the preparation loop is the same — baseline practice test, targeted study, re-test, book. Our guide to using practice exams properly covers that loop in detail.
Does a harder exam impress employers more?
Only when the difficulty is relevant to the job. A hiring manager for an Azure shop is more moved by AI-103 than by a harder exam on a cloud they do not run. Recruiters pattern-match credential names against the job spec; almost none of them carry a difficulty ranking in their heads. Difficulty buys you something real — deeper skills, more confidence under scenario pressure — but as a signal it only pays when the work matches. Stack relevance beats difficulty, every time it is tested.
Where difficulty rankings go wrong
Including this one: difficulty is a judgment call, not a measurement. Vendors do not publish pass rates, candidates differ wildly in background, and the same exam that flattens a career changer is a weekend formality for a platform engineer. Rankings like ours are useful for sequencing — which rung follows which — and dangerous as a scoreboard.
The scoreboard instinct is the real trap. Certification forums celebrate collecting the hardest badges the way gamers chase achievements, and it produces engineers with five credentials on clouds they will never touch professionally. The exam that pays is the one your next job spec names. Everything else is an expensive hobby — a fine hobby, but be honest that that is what it is.
Verdict
If you want the easiest legitimate proctored credential and do not code, take the AWS AI Practitioner — no code, free official prep; Azure's equivalent, now AI-901, expects basic Python. If you can build, enter the ladder at your platform's associate exam and ignore the rungs below you. Reserve the top band for when your work genuinely lives there. The full field, ranked by value rather than difficulty, is in our 2026 rankings; the staged path from beginner to advanced is the AI certification roadmap; and if you want a personalised rung, the Picker takes a sentence about your situation and points you at one.
Ready to start?
Bought once, with what Udemy calls lifetime access. Udemy's price swings between its list price and a sale price, sometimes within days — check it on the day rather than trusting any figure you read, here or anywhere else.
Frequently asked questions
What is the easiest AI certification exam?
AWS Certified AI Practitioner (AIF-C01). It is foundational, AWS puts coding out of scope, and there is free official preparation on AWS Skill Builder. Azure AI Fundamentals used to hold this spot, but its exam is now AI-901, which replaced AI-900 on 30 June 2026 and expects basic Python. Among proctored, employer-recognised AI credentials, nothing on the current ladder is gentler than AIF-C01.
“Proctored and employer-recognised” is doing the work in that sentence. Plenty of credentials are easier — a completion certificate for a video course asks nothing of you at all — and none of them carries the same weight, because nobody watched you take it. AIF-C01 is the gentlest exam where somebody verified you sat it without help, which is the whole difference between the two categories.
What is the hardest AI certification?
In our judgment, the Google Cloud Professional Machine Learning Engineer. It is professional-tier with no associate stepping stone, tests scenario judgment across the whole ML lifecycle, and assumes real hands-on experience. NVIDIA's NCP professional tracks compete for the title in the infrastructure lane.
The missing stepping stone is most of why it tops this list. Other vendors let you climb — foundational, then associate, then professional — with each rung teaching the exam style of the next; this one asks you to arrive at professional tier having learned that style somewhere else. Candidates who prepare purely from courses commonly fail it once before passing, which is worth budgeting for rather than being surprised by.
Is AI-102 much harder than AI-900?
It was — both have now been retired (AI-900 on 30 June 2026), and while they ran it was the largest single jump on the ladder. AI-900 tested concept recognition with no code; AI-102 assumed you could write Python or C# and implement Azure AI services. They were different exam types, not adjacent difficulty levels.
That jump still exists, now between AI-901 and AI-103, which replaced AI-102 at the associate tier — though AI-901's basic Python narrows it. It is the general shape of these ladders rather than a quirk of one exam: somewhere between the fundamentals and associate tiers, every vendor stops asking whether you recognise a concept and starts assuming you have built something. Find out which side of that line your next exam sits on.
Are AI certification pass rates published?
Generally no — the major vendors do not publish pass rates for these exams, which is why every difficulty ranking, including ours, is editorial judgment rather than measurement. Use rankings for sequencing decisions, and weight testimony from people whose background resembles yours.
Background matters more than any published figure would anyway, which is the deeper reason the absence is tolerable. The same paper is straightforward for someone who configures these services weekly and opaque for someone who has only read about them, so a single pass rate would average two populations that have nothing in common. One account from somebody with your experience is worth more than a number covering everybody.
Should I start with the easiest exam?
Only if you cannot code. Non-technical readers should start at a foundational exam that needs no code, such as the AWS AI Practitioner, and often stop there. Working developers should skip the foundational rungs and enter at their platform's associate exam — the lower exams add cost and weeks without adding much signal.
The exception is when you are new to the platform rather than new to the field. A senior developer meeting a cloud's AI services for the first time gets real value from the foundational syllabus as a map, even though the certificate adds nothing to their CV — so read the free material and skip the exam fee. Study the rung; do not always buy it.
Keeping this current. Course formats, prices, and certification exam fees change and vary by region. We review our guides regularly, and we always recommend confirming the specifics on the provider's official page before you enrol.