Over the past five months, I have completed three generative AI certifications from three of the largest AI companies:
• AWS Certified AI Practitioner
• Google Generative AI Leader Certification
• NVIDIA Certified Associate in Generative AI LLMs
I was asked how they differ and which one is “best”. The short answer is that they are designed for different use cases, even though they look similar on the surface.
First, what they have in common
All three cost roughly the same, around $100 per exam.
All three are proctored, timed exams, either online with screen sharing or at a test centre.
All three use a similar format of roughly 50 to 70 multiple-choice questions, with a pass mark of around 70 percent.
Where they differ is what they are optimised to teach.
AWS Certified AI Practitioner
Of the three, this is comfortably the most hands-on and technical.
AWS also provides the most thorough preparation by far. The learning pathways are free and combine theory, practice exams, interactive elements, and real hands-on building using services like Bedrock. You actively configure guardrails, choose knowledge bases, and build working GenAI applications.
This is the best option if you want to understand the end-to-end process of building and deploying AI products in a real cloud environment.
My preparation took around three weeks, a few hours a week, with significant practical work.
Google Generative AI Leader Certification
This is the most broad and accessible of the three.
The training is open source, free, well designed, and suitable regardless of technical background. It focuses on understanding AI concepts, use cases, risks, and Google’s ecosystem, without requiring you to build anything yourself.
This is ideal if you want to understand GenAI quickly, especially from a leadership or decision-making perspective.
I completed the training and the exam on the same day.
NVIDIA Certified Associate in Generative AI LLMs
This is the most theoretical certification.
It goes deep into algorithms, statistics, training data, attention-based models, and core machine learning concepts. It is very strong from an educational and conceptual standpoint.
One caveat is that NVIDIA’s official training sits behind a paywall, with multiple paid modules. I chose to prepare using open-source material instead, which worked well. If you followed the full NVIDIA training path, this would likely be the longest and most expensive route.
This is the best option if you want a deep theoretical understanding of how GenAI and ML models actually work, rather than how to deploy them.
In summary
If you want fast, high-level understanding and limited time, choose Google.
If you want deep theoretical and educational grounding, choose NVIDIA.
If you want hands-on, practical experience building real AI products, AWS is comfortably the strongest.
Happy to compare notes with others who have taken a different path.