Best AI Certifications

Google's Professional Machine Learning Engineer is the AI certification employers take most seriously. The two-hour exam (50 to 60 questions) is built on realistic scenarios: you are given a business problem and must choose how to prepare data, train, deploy, automate and monitor a model on Google Cloud, mostly with Vertex AI. It does not ask you to write code, but Google recommends Python and SQL so you can read code snippets in the questions.

Google recommends three or more years of industry experience, including at least one year on Google Cloud. That makes it a poor first certification, but a strong signal for mid-career engineers. Salary surveys from training companies such as Skillsoft regularly put it among the higher-paying cloud certs, though those surveys are self-reported.

The weak spots: it is tied to Google's tools, generative AI is only part of the syllabus, and you must renew every two years.

Pick it if you work, or want to work, as an ML engineer and your target companies use Google Cloud. Skip it if you are new to ML; start with the AWS AI Practitioner or a course instead.

AI-103 is Microsoft's new developer exam for building generative AI apps and agents, and it replaced the long-running AI-102 when that retired on 30 June 2026. It earns the Azure AI Apps and Agents Developer Associate title. The syllabus covers planning and managing Azure AI solutions, building generative AI and agentic solutions in Microsoft Foundry, and older skills such as computer vision, text analysis and document extraction. Microsoft expects Python development experience.

It is the most current developer cert on this list, and it keeps Microsoft's strong renewal deal: associate certifications renew each year through a free online assessment, so you never pay for the exam twice. The US fee is $165, and the official training and practice test on Microsoft Learn are free.

The risk is newness. Recruiters and job ads still often ask for "AI-102", so for a while you may need to explain that AI-103 is its successor.

Pick it if you build AI features on Azure or want a current, hands-on developer credential. Skip it if your employers use AWS or Google Cloud, or you have never coded in Python.

With the AWS Machine Learning Specialty retired on 31 March 2026, the Machine Learning Engineer Associate is now AWS's main ML certification. It tests how you prepare data, train, deploy and monitor models on AWS, mostly with SageMaker, and it is valid for three years.

Timing matters right now. The current MLA-C01 exam is 130 minutes and 65 questions for $150, but its last English sitting is 28 September 2026. AWS opened beta registration for MLA-C02 on 1 September 2026: it is longer (170 minutes, 85 questions), adds generative AI, agentic AI, foundation models and responsible AI, and costs $75 during the beta. If you are not ready this week, prepare for C02.

AWS is the largest cloud, so this cert shows up in many job ads. It is slightly less demanding than Google's Professional ML Engineer, which is why it sits just below.

Pick it if your employer or target companies run on AWS. Skip it if you only want a business-level AI credential; the AI Practitioner is enough.

The AWS Certified AI Practitioner is the best first AI certification for most people. It checks that you understand AI, machine learning and generative AI concepts, when to use them, responsible AI basics, and which AWS services (such as Amazon Bedrock and SageMaker) fit which job. There is no coding.

The exam is 90 minutes and 65 questions, 15 of which are unscored trial questions. It costs $100, lasts three years, and is offered in 12 languages. AWS's own free Skill Builder courses are enough preparation for many candidates.

Its value is recognition, not depth. Hiring managers see the AWS name and know you understand the vocabulary, but it will not prove you can build anything. For technical roles, treat it as a stepping stone to the ML Engineer Associate.

Pick it if you are new to AI, or work in product, sales, consulting or management around AI projects. Skip it if you already build ML systems; go straight to an associate or professional exam.

AI-901 replaced the very popular AI-900 on 30 June 2026 and still earns the Azure AI Fundamentals certification. The big change is that it is no longer purely conceptual: more than half the exam (55 to 60%) is about building AI solutions in Microsoft Foundry, and Microsoft now expects some knowledge of Python syntax and Azure resources.

That makes AI-901 a better signal than AI-900 was, and a natural first step before AI-103. Like all Microsoft fundamentals certifications, it never expires. Training, a practice assessment and an exam sandbox are all free on Microsoft Learn. The English exam was last updated on 15 April 2026.

The downside is that it is now less friendly to non-technical candidates. If you have never coded, the AWS AI Practitioner or Google Generative AI Leader will feel easier.

Pick it if you plan to build on Azure and want a low-cost first credential that never lapses. Skip it if you are in a business role with no coding; choose the Google or AWS entry exams.

This is the hardest generative AI certification on the list. The AWS Certified Generative AI Developer Professional tests whether you can put foundation models into real production applications: choosing models, building retrieval (RAG) and agents, controlling cost and latency, security, and evaluation, mostly with Amazon Bedrock. The exam is three hours and 75 questions, and the pass mark (750) is higher than on AWS associate exams.

AWS recommends two or more years building production apps and a year of hands-on generative AI work. At $300, it is also the most expensive exam here. In return, it is a strong, current signal for senior engineers, and it fills the gap left by the retired ML Specialty.

Recognition is still building because the exam is new. It scores below the ML Engineer Associate overall only because it is expensive and closed to beginners.

Pick it if you already ship GenAI features on AWS and want to prove it. Skip it if you have less than a year of hands-on generative AI work.

Google's Generative AI Leader is a business-level certification for people who plan or sponsor AI projects rather than build them. The 90-minute exam covers generative AI fundamentals, Google Cloud's AI products, ways to improve model output (such as grounding and prompting), and business strategy for rolling AI out. Google says it suits anyone in any job role, with no prerequisites.

It costs $99, lasts three years, and Google's learning path is free. That makes it the cheapest credible AI credential for a manager, consultant or salesperson who needs to talk about AI with confidence.

The catch is rigour. It is a multiple-choice knowledge exam, and a good share of it is about Google's own product names. Technical hiring managers will not weigh it heavily.

Pick it if you lead or sell AI projects and want a quick, recognised badge. Skip it if you are an engineer; take the Professional ML Engineer instead.

NVIDIA's Generative AI LLMs Associate is a one-hour, remotely proctored exam on how large language models work and how to build with them: architecture, prompting, fine-tuning, retrieval (RAG), deployment, evaluation and ethics, plus a slice on NVIDIA's own software. It costs $125 and has no formal prerequisites, though basic ML and Python help.

Its appeal is that it is less tied to one cloud than the AWS, Google and Microsoft exams. It also sits naturally next to NVIDIA's Deep Learning Institute courses, and there is a Professional-level version for more experienced engineers.

The limits: it is still a short multiple-choice test, it lasts only two years and you renew by retaking it, and it is less often named in job ads than the big three cloud certifications.

Pick it if you want a quick, fairly neutral LLM credential or work with NVIDIA's stack. Skip it if your target employers standardise on one cloud; that cloud's cert will count for more.

If your company runs its data on Databricks, this associate exam is a practical credential. It tests the Databricks machine learning workflow: using the platform's ML tools, preparing features, developing and tuning models, and deploying them. Training-provider guides put the format at 90 minutes and 45 questions, and Databricks charges $200.

Databricks is common in data-heavy companies, so the cert has real value in data science and data engineering hiring. It is also a useful complement to a cloud cert, since Databricks runs on AWS, Azure and Google Cloud.

Outside the Databricks world, though, it means much less. It lasts only two years, and its syllabus is focused on classic ML rather than generative AI (Databricks has a separate generative AI engineer exam).

Pick it if you work in a Databricks shop. Skip it if you are not sure you will use Databricks; a cloud ML certification is a safer bet.

IBM's AI Engineering Professional Certificate is a different kind of credential: a 13-course Coursera programme rather than a single proctored exam. Over roughly four months at 10 hours a week it covers machine learning, deep learning with Keras, PyTorch and TensorFlow, computer vision, NLP, large language models, RAG and Apache Spark, with hands-on labs and capstone projects. Learners rate it 4.6 out of 5 from more than 22,000 reviews.

The structure is its strength. If you want to learn and earn a credential at the same time, it is more complete than any exam-only route, and the certificate does not expire.

As a signal to employers, though, it is weaker. There is no proctored exam, so a hiring manager cannot be sure who did the work, and IBM's AI brand carries less weight in job ads than AWS, Google or Microsoft.

Pick it if you are changing careers and want a guided path with projects for your portfolio. Skip it if you already have the skills and just need proof; a proctored cloud exam is cheaper and counts for more.