If you want to prove you can work with AI on AWS, the certification catalog can feel crowded. This post gives you a map: which credentials are aimed at AI, who each one is for, what the exams look like, and in which order a sensible person would take them. Every figure below comes from the official AWS certification pages as they stood on 25 October 2026, so always double-check the live pages before you book anything.
The short version
Two certifications are built squarely around AI on AWS, and they sit at opposite ends of the difficulty scale:
- AWS Certified AI Practitioner (AIF-C01) is the foundational, concept-level exam.
- AWS Certified Generative AI Developer – Professional (AIP-C01) is the advanced, build-it-in-production exam.
Around them sits a ring of neighbouring certifications that make good stepping stones, plus a newer business-focused AI credential and a set of hands-on "Demonstrated" credentials.
At a glance
| Detail | AI Practitioner | GenAI Developer – Professional |
|---|---|---|
| Level | Foundational | Professional |
| Duration | 90 minutes | 180 minutes |
| Questions | 65 | 75 (65 scored + 10 unscored) |
| Exam fee | 100 USD | 300 USD |
| Format | Online proctored or Pearson VUE test centre | Online proctored or Pearson VUE test centre |
| Languages | 12 (including English, Japanese, Korean, Spanish, Portuguese, French, Arabic and Chinese variants) | English, Japanese, Korean, Simplified Chinese |
| Validity | 3 years | See the AWS recertification policy |
The Italian and German versions of the AI Practitioner exam were announced to retire after 15 October 2026, so if you were planning to sit it in either language, check availability first.
AWS Certified AI Practitioner
This exam validates foundational knowledge of AI, machine learning and generative AI concepts, along with the AWS services that support them. AWS positions it for people who use AI/ML technologies on AWS but do not necessarily build solutions with them: people in cloud, development, data, IT, AI/ML or line-of-business roles.
Good reasons to start here:
- You are new to AI on AWS and want a structured map of the vocabulary and services.
- You work near AI projects (product, sales engineering, project management) and need credible fluency.
- You want a low-cost first win before a harder exam.
If you are also new to IT and the cloud in general, AWS suggests starting with Cloud Practitioner Essentials or Technical Essentials first. If you already hold Cloud Practitioner or any Associate-level certification, you can skip the cloud basics and go straight to the AI foundations.
The exam is also different from AWS Certified Cloud Practitioner: that exam touches AI in only a single task statement, whereas the entire AI Practitioner outline is about AI, ML and generative AI.
AWS Certified Generative AI Developer – Professional
This is the flagship credential for builders. According to the exam guide, it validates your ability to integrate foundation models into applications and business workflows and to run those solutions in production. The listed skills include:
- Designing solutions with vector stores, retrieval augmented generation (RAG) and knowledge bases
- Integrating foundation models into applications and workflows
- Applying prompt engineering and prompt management
- Implementing agentic AI solutions
- Optimising GenAI applications for cost, performance and business value
- Applying security, governance and Responsible AI practices
- Troubleshooting, monitoring and optimising GenAI applications
- Evaluating foundation models for quality and responsibility
The target candidate has two or more years of experience building production-grade applications on AWS or with open-source tools, general AI/ML or data engineering experience, and at least one year of hands-on work with generative AI. Notably, model development and training, advanced ML techniques, and data/feature engineering are explicitly out of scope. This is an exam about building applications on top of foundation models, not about training them.
The content is split across five domains:
| Domain | Weight |
|---|---|
| 1. Foundation Model Integration, Data Management, and Compliance | 31% |
| 2. Implementation and Integration | 26% |
| 3. AI Safety, Security, and Governance | 20% |
| 4. Operational Efficiency and Optimization for GenAI Applications | 12% |
| 5. Testing, Validation, and Troubleshooting | 11% |
Scoring is on a scale of 100 to 1,000 with a passing score of 750. The model is compensatory, meaning you do not have to pass each domain individually, only the exam overall. Unanswered questions count as wrong and there is no penalty for guessing, so never leave a blank. Questions are either multiple choice (one correct answer) or multiple response (two or more correct answers, all of which must be selected for credit).
No prerequisite certification is required. AWS suggests that AI Practitioner, Solutions Architect – Associate, Machine Learning Engineer – Associate and Data Engineer – Associate can all help before you attempt it.
The neighbouring certifications worth knowing
These are not AI-only exams, but they appear in AWS's own recommended paths:
- Solutions Architect – Associate: the usual recommendation for people moving into cloud careers, and a strong base for the networking, security and deployment knowledge the Professional exam assumes.
- Machine Learning Engineer – Associate: for people on the data/AI/ML track. Passing it automatically recertifies AI Practitioner.
- Data Engineer – Associate: the other data/AI/ML track recommendation, useful for the data pipelines that feed RAG systems.
- AI Business Strategist: a different kind of credential. It validates business judgement for AI decisions (which investments to pursue, how to build the business case, how to govern risk, how to move from pilot to production) and does not assess knowledge of AWS AI services. It complements AI Practitioner rather than replacing it.
AWS also lists hands-on credentials such as AWS Agentic AI Demonstrated, AWS MLOps Demonstrated, AWS Securing Agent Identities Demonstrated and AWS Databases for Agentic AI Demonstrated. These are timed, practical challenges that show what you can actually do, and they sit alongside the traditional certifications rather than replacing them.
Which path should you take?
There is no single right answer, but three common routes make sense:
- Newcomer to cloud and AI: Cloud Practitioner Essentials → AI Practitioner → Solutions Architect – Associate → build real projects → GenAI Developer – Professional.
- Developer or cloud engineer already comfortable on AWS: AI Practitioner (optional, for vocabulary and a quick win) → hands-on work with Amazon Bedrock → GenAI Developer – Professional.
- Data or ML practitioner: Machine Learning Engineer – Associate and/or Data Engineer – Associate → GenAI Developer – Professional.
Whichever you pick, the Professional exam rewards hands-on experience far more than memorisation. Build something with foundation models, retrieval and guardrails before you sit it.
How AWS suggests you prepare
Both exams follow the same four-step Exam Prep Plan on AWS Skill Builder: get to know the exam with the official guide and sample questions, refresh your AWS knowledge with the pretest and digital courses, review and practise by domain, then assess readiness with the official practice exam. Labs, Cloud Quest, Jam and SimuLearn provide practical reinforcement.
What's next in this series
In Post 2 we will unpack the AI Practitioner exam guide domain by domain, so you know exactly what is in scope before you study a single service. From there we move into foundations (what a foundation model actually is, tokens, embeddings, prompts) before getting into Amazon Bedrock itself.
Sources: the official AWS pages for AWS Certified AI Practitioner and AWS Certified Generative AI Developer – Professional, and the AIP-C01 exam guide on docs.aws.amazon.com. Prices, languages and policies change, so verify them on AWS before booking.
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