AWS Certified Generative AI Developer - Professional

AWS Certified Generative AI Developer - Professional Exam

The AWS Certified Generative AI Developer – Professional (AIP-C01) certification assesses advanced technical proficiency in designing, building, and deploying production-ready generative AI solutions on AWS. This certification demonstrates a candidate's practical knowledge of implementing GenAI solutions into production environments using AWS technologies, with a particular focus on the effective integration of foundation models (FMs) into applications and business workflows. 

Who should take the exam?

• The target candidate should have two or more years of experience building production-grade applications on AWS or with open-source technologies, along with general AI/ML or data engineering experience
• A candidate should have a minimum of one year of hands-on experience implementing generative AI solutions. 
• This credential is particularly suited to professionals who are ready to move beyond proofs-of-concept and deliver generative AI systems that produce measurable business outcomes — while upholding the highest standards of security, governance, and responsible AI practices.

Skills Required

Candidates are expected to possess proficiency in the following technical areas prior to sitting the examination:

• Deep, hands-on experience with Amazon Bedrock and its integration into enterprise applications and workflows
• Familiarity with AWS vector store offerings and the construction of knowledge bases for generative AI models
• Ability to evaluate and optimise GenAI applications for cost and performance
• Understanding of security, governance, and responsible AI implementation on AWS
• Working knowledge of AWS serverless offerings for event orchestration, API development, and scalability

Knowledge Gained

Upon successful completion of this certification, professionals will have demonstrated and consolidated expertise in the following areas:

• Designing and implementing solutions using vector stores, Retrieval Augmented Generation (RAG), knowledge bases, and other GenAI architectures
• Integrating foundation models into enterprise applications and business workflows
• Applying prompt engineering and prompt management techniques
• Implementing agentic AI solutions
• Optimising generative AI applications for cost, performance, and business value
• Implementing security, governance, and Responsible AI practices
• Troubleshooting, monitoring, and optimising GenAI applications in production
• Evaluating foundation models for quality, accuracy, and responsible use