AWS has just announced one of its biggest certification updates in years: the retirement of the AWS Certified Machine Learning Specialty and the introduction of a completely new credential: the AWS Certified Generative AI Developer Professional.
This marks a clear shift in how AWS and the wider tech world approaches artificial intelligence. Instead of focusing on traditional machine learning theory, AWS is now spotlighting practical, real-world generative AI development. Here’s what we know so far, what’s changing, and how you can prepare.
What we know about the new certification
The AWS Certified Generative AI Developer Professional is set to launch in beta on November 18, 2025, with full availability expected after that phase. According to AWS, this certification is designed for professionals who already have around two years of cloud experience and at least one year of hands-on work with generative AI.
Like other professional-level AWS certifications, the exam will likely take roughly 200 minutes and include around 85 questions. What’s more, AWS plans to retire the Machine Learning Specialty exam on March 31, 2026, making this new certification its direct replacement.
The new credential focuses on validating your ability to build production-ready generative AI solutions that deliver measurable business value. Expect it to assess how well you can balance cost, security, and scalability when designing and deploying generative AI applications on AWS.
Why AWS is replacing the ML Specialty exam
The Machine Learning Specialty exam was introduced in 2019 when the field of AI looked very different. Back then, the focus was on data science theory, algorithm selection, and model tuning. It made sense at a time when organizations needed experts who could build and train models from scratch.
But the industry has moved on. Today, the demand isn’t for researchers building neural networks in isolation. It’s for developers and engineers who can integrate foundation models, deploy generative AI services, and build solutions that solve real-world business problems.
That’s why AWS is adapting. The new certification aligns with today’s practical needs: hands-on implementation, cloud integration, and generative AI deployment. It reflects the reality that most companies now rely on pre-trained models and APIs from platforms like Amazon Bedrock, rather than building their own from the ground up.
Likely topics covered in the exam
AWS hasn’t yet published the official exam guide, but based on the focus and messaging, we can make some informed predictions about the content areas.
You can expect the new certification to include:
Amazon Bedrock – working with foundation models, deploying them at scale, and connecting them to enterprise data sources.
Vector databases and RAG (Retrieval-Augmented Generation) – building intelligent systems that combine private data with generative AI to deliver accurate, context-aware results.
Prompt engineering and model evaluation – designing and refining prompts to get consistent, high-quality outputs while understanding how to measure performance and reliability.
Integration with AWS services – tying together Bedrock, Lambda, API Gateway, S3, DynamoDB, or SageMaker endpoints to create end-to-end AI applications.
Security and cost management – protecting sensitive data, managing IAM permissions, and optimizing inference costs to stay within budget.
Monitoring and governance – maintaining ethical AI practices, ensuring compliance, and monitoring for performance drift over time.
If you’re familiar with the Machine Learning Specialty exam, some themes will feel familiar, such as data preparation, deployment, and evaluation, for instance. However, everything will now be framed through the generative AI lens. The emphasis will move away from theoretical modeling and toward deploying working AI applications on AWS.
How to start preparing now
Even though the official exam guide hasn’t been released, there’s already plenty you can do to get a head start.
Get hands-on with AWS Bedrock. Start experimenting with Amazon Bedrock, AWS’s generative AI platform that allows you to use foundation models from providers like Anthropic (Claude) and Amazon (Titan). Build small proof-of-concept projects to understand how to integrate models, manage inputs, and deploy outputs via APIs.
Learn about vector databases and RAG. Understanding how to combine your own data with foundation models is essential. Try integrating Amazon OpenSearch Serverless, Aurora ML, or other vector database tools with generative AI apps.
Strengthen your AWS fundamentals. You’ll still need a solid grasp of networking, IAM, compute, and security. These core skills will remain just as important in a generative AI context.
Brush up on Python and APIs. Python remains the go-to language for AI orchestration. Knowing how to connect APIs, handle data, and automate workflows will make you far more effective when building AI-driven applications.
Stay updated. AWS will release the official exam guide closer to the beta launch in November 2025. That’s when we’ll get confirmation of domains, objectives, and example questions.
In the meantime, if you’re still building your foundation, certifications like the AWS Certified Solutions Architect Associate or Developer Associate are excellent stepping stones before tackling a professional-level exam.
Building your future with generative AI skills
Generative AI is no longer a research topic. It’s driving real business transformation. Companies across industries are deploying AI assistants, content generators, data insight tools, and intelligent chat interfaces. These solutions must be reliable, secure, and cost-efficient and they need skilled professionals who can make them work on the cloud.
That’s where the AWS Certified Generative AI Developer Professional comes in. It validates the blend of AI and cloud expertise that’s quickly becoming one of the most in-demand skill sets in tech. This certification marks the next phase of AI on AWS – where success is measured not by model accuracy in isolation, but by how well you can deploy and scale AI-powered solutions in production.
If you can combine cloud engineering experience with practical generative AI implementation, you’ll be positioned right at the intersection of two of the fastest-growing fields in technology. The best way to prepare isn’t just by studying for the exam. I’s by gaining structured, hands-on experience that helps you turn theory into practice.
That’s exactly what the Cloud Mastery Bootcamp from Digital Cloud Training is designed to do. It helps aspiring and experienced professionals build the skills needed to get job-ready in cloud computing – with guided learning paths, hands-on projects, and direct support from expert instructors. Whether your goal is to advance your AWS career or develop real-world cloud solutions that stand out to employers, the Cloud Mastery Bootcamp provides the framework and mentorship to help you succeed.
Generative AI is reshaping the cloud landscape, and those who start learning now will lead the next wave of innovation. If you’re ready to take your cloud skills to the next level and prepare for the future of AI on AWS, the Cloud Mastery Bootcamp is the perfect place to start.