AWS Certified Generative AI Developer - Professional
Practice questions and flashcards for the AWS Certified Generative AI Developer - Professional (AIP-C01) exam. Built from the official AWS exam guide — covers foundation model integration, RAG and vector stores, agentic AI with Amazon Bedrock AgentCore, AI safety and governance, cost optimization, and GenAI evaluation.
AIP-C01 (AWS Certified Generative AI Developer - Professional) is a professional-tier AWS certification that validates the ability to integrate foundation models into production applications on AWS — RAG architectures, vector stores, agentic systems, prompt governance, AI safety, observability, and cost optimization. It does not cover training models from scratch. 75 questions, 180 minutes, passing score 750/1000, $300 USD.
| Domain | Weight | Items | Coverage |
|---|---|---|---|
Foundation Model Integration, Data Management, and Compliance | 31% | 0 items | |
Implementation and Integration | 26% | 0 items | |
AI Safety, Security, and Governance | 20% | 0 items | |
Operational Efficiency and Optimization for GenAI Applications | 12% | 0 items | |
Testing, Validation, and Troubleshooting | 11% | 0 items |
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Correct answer: Claude 3.5 Sonnet via Amazon Bedrock. This is one of 1029 practice questions in the AIP-C01 AIP-C01: AWS Generative AI Developer Professional pack on ReadRoost.
The AIP-C01 AIP-C01: AWS Generative AI Developer Professional study pack on ReadRoost includes 1029 practice questions and 517 flashcards, covering 5 exam domains including Foundation Model Integration, Data Management, and Compliance. Every question has a detailed explanation so you understand why each answer is right or wrong.
Yes. The AIP-C01 AIP-C01: AWS Generative AI Developer Professional pack is mapped to the latest official exam objectives and is maintained by the ReadRoost team. You get flashcards with spaced repetition, timed practice exams, and AI-powered explanations.
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A company is building an application that must analyze scanned PDF invoices containing both text and tables, then generate structured JSON summaries. They want to use a fully managed foundation model in Amazon Bedrock without preprocessing the images separately. Which model should they select?