Track AWS AIP-C01 practice status for Generative AI Developer Professional and request IT Mastery coverage.
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AIP-C01 is AWS Certified Generative AI Developer - Professional. It validates advanced technical expertise in building and deploying production-ready generative AI solutions on AWS, especially solutions that integrate foundation models into applications and business workflows using services such as Amazon Bedrock.
This page tracks the AIP-C01 practice-bank rollout for IT Mastery. Dedicated simulator practice is not live yet, but you can review the exam snapshot, topic coverage, and related live AWS practice options while coverage is being prioritized.
Who AIP-C01 is for
developers building production-grade generative AI applications on AWS
candidates with AWS application experience who need deeper Amazon Bedrock, RAG, agent, governance, monitoring, and optimization judgment
teams moving from AI proofs of concept into secure, observable, cost-aware production GenAI systems
AIP-C01 exam snapshot
Vendor: AWS
Official exam name: AWS Certified Generative AI Developer - Professional
Exam code: AIP-C01
Category: Professional
Items: 75 total, including 65 scored and 10 unscored
Exam time: 180 minutes
Question types: multiple-choice and multiple-response
Passing score: 750 scaled
Current IT Mastery status: full simulator not yet live
AIP-C01 questions should reward production-grade GenAI decisions: choosing the right foundation model integration pattern, grounding and evaluating outputs, securing data and identities, controlling cost and latency, and troubleshooting deployed AI workflows.
Topic coverage for AIP-C01
Domain
Weight
Foundation Model Integration, Data Management, and Compliance
31%
Implementation and Integration
26%
AI Safety, Security, and Governance
20%
Operational Efficiency and Optimization for GenAI Applications
12%
Testing, Validation, and Troubleshooting
11%
What AIP-C01 questions usually test
selecting the right GenAI architecture: RAG, knowledge bases, vector stores, agents, prompt workflows, or direct model integration
integrating foundation models into applications and business workflows without losing security, auditability, or operational control
applying responsible AI, governance, access control, and data-protection practices around GenAI systems
optimizing GenAI applications for cost, latency, throughput, quality, and business value