AWS · AIP-C01 · Advanced
AWS Generative AI Developer - Professional (AIP-C01) — Practice Questions and Mock Exam
Practice real AIP-C01 questions, never dumps. Alex explains every answer, and your readiness score tells you when you're ready to pass.
Checked against AWS · July 2026Current exam version
Overview
About the exam
The AWS Certified Generative AI Developer – Professional validates advanced expertise in building, deploying, and optimizing production-grade generative AI applications on AWS. It covers solution design using foundation models, implementing generative AI with Amazon Bedrock and SageMaker, retrieval-augmented generation architectures, API integration, and security and governance for AI systems.
This certification is designed for developers and architects with at least one year of hands-on experience implementing generative AI solutions on AWS. It is distinct from the foundational AIF-C01, focusing on practical implementation of generative AI applications using AWS services.
Exam Domains
What's on the exam
The exam consists of 75 questions (65 scored, 10 unscored) over 180 minutes, featuring multiple-choice and multiple-response question types. Questions are deeply scenario-based, covering Bedrock integration, RAG architecture design, model customization, and production deployment patterns. Budget 2.4 minutes per question and manage time carefully.
SourceAWS exam page
Format
What to expect
Watch out
Where candidates struggle
This professional-level exam requires hands-on generative AI implementation experience. Candidates must go beyond conceptual understanding to demonstrate practical ability to build, optimize, and secure production generative AI systems.
- 01Bedrock Configuration — Not understanding Bedrock model selection, inference parameters, guardrails configuration, and knowledge base integration for different generative AI use cases.
- 02RAG Architecture — Misunderstanding vector store selection, embedding model choices, chunking strategies, and retrieval pipeline design for retrieval-augmented generation.
- 03Model Customization — Confusing when to use prompt engineering, fine-tuning, continued pre-training, or distillation for different model customization requirements.
- 04Cost Management — Not understanding token-based pricing, provisioned throughput, model selection trade-offs, and caching strategies for optimizing generative AI costs.
- 05AI Governance — Overlooking guardrails, content filtering, PII handling, and audit logging requirements for production generative AI applications.
Details
Exam logistics
Delivered via Pearson VUE online or at testing centers. Available in English and Japanese; additional languages may be added over time. The certification is valid for 3 years with renewal through recertification exams.
SourceAWS exam page
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