AWS Certified Generative AI Developer - Professional (AIP-C01)
The AWS Certified Generative AI Developer - Professional (AIP-C01) exam is intended for individuals who perform a GenAI developer role. The exam validates a candidate's ability to effectively integrate foundation models (FMs) into applications and business workflows. This certification demonstrates practical knowledge of how to implement GenAI solutions into production environments by using AWS technologies.
Topics
Introduction
The AWS Certified Generative AI Developer - Professional (AIP-C01)
The exam also validates a candidate's ability to complete the following tasks:
Design and implement solutions by using vector stores, Retrieval Augmented Generation (RAG), knowledge bases, and other GenAI architectures.
Integrate FMs into applications and business workflows.
Apply prompt engineering and management techniques.
Implement agentic AI solutions.
Optimize GenAI applications for cost, performance, and business value.
Implement security, governance, and Responsible AI practices.
Troubleshoot, monitor, and optimize GenAI applications.
Evaluate FMs for quality and responsibility.
Target candidate description
The target candidate should have 2 or more years of experience building production-grade applications on AWS or with open-source technologies, general AI/ML or data engineering experience, and 1 year of hands-on experience implementing GenAI solutions.
Recommended AWS knowledge
The target candidate should have the following AWS knowledge:
Experience with AWS compute, storage, and networking services
Understanding of AWS security best practices and identity management
Experience with AWS deployment and infrastructure as code (IaC) tools
Familiarity with AWS monitoring and observability services
Understanding of AWS cost optimization principles
Job tasks that are out of scope for the target candidate
The following list contains job tasks that the target candidate is not expected to be able to perform. This list is non-exhaustive. These tasks are out of scope for the exam:
Model development and training
Advanced ML techniques
Data engineering and feature engineering
Refer to the Appendix for a list of technologies and concepts that might appear on the exam, a list of in-scope AWS services and features, and a list of out-of-scope AWS services and features.
Exam content
Question types
The exam contains one or more of the following question types:
Multiple choice: Has one correct response and three incorrect responses (distractors).
Multiple response: Has two or more correct responses out of five or more response options. You must select all the correct responses to receive credit for the question.
Ordering: Has a list of 3–5 responses to complete a specified task. You must select the correct responses and place the responses in the correct order to receive credit for the question.
Matching: Has a list of responses to match with a list of 3–7 prompts. You must match all the pairs correctly to receive credit for the question.
Unanswered questions are scored as incorrect. There is no penalty for guessing. The exam includes 65 questions that affect your score.
Unscored content
The exam includes 10 unscored questions that do not affect your score. AWS collects information about performance on these unscored questions to evaluate them for future use as scored questions. The unscored questions are not identified on the exam.
Exam results
The AWS Certified Generative AI Developer - Professional (AIP-C01) exam has a pass or fail designation. The exam is scored against a minimum standard established by AWS professionals who follow certification industry best practices and guidelines.
Your results for the exam are reported as a scaled score of 100–1,000. The minimum passing score is 750. Your score shows how you performed on the exam as a whole and whether you passed. Scaled scoring models help equate scores across multiple exam forms that might have slightly different difficulty levels.
Your score report could contain a table of classifications of your performance at each section level. The exam uses a compensatory scoring model, which means that you do not need to achieve a passing score in each section. You need to pass only the overall exam.
Each section of the exam has a specific weighting, so some sections have more questions than other sections have. The table of classifications contains general information that highlights your strengths and weaknesses. Use caution when you interpret section-level feedback.
Content outline
This exam guide includes weightings, content domains, tasks, and skills for the exam. This guide does not provide a comprehensive list of the content on the exam.
The exam has the following content domains and weightings:
Content Domain 1: Foundation Model Integration, Data Management, and Compliance (31% of scored content)
Content Domain 2: Implementation and Integration (26% of scored content)
Content Domain 3: AI Safety, Security, and Governance (20% of scored content)
Content Domain 4: Operational Efficiency and Optimization for GenAI Applications (12% of scored content)
Content Domain 5: Testing, Validation, and Troubleshooting (11% of scored content)
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