AWS Certified Generative AI Developer - Professional (AIP-C01)¶
The AWS Certified Generative AI Developer - Professional (AIP-C01) certification validates the ability to integrate foundation models (FMs) into production applications and business workflows on AWS. It is a Professional-tier credential targeted at developers who already build production-grade applications and have hands-on experience implementing GenAI solutions.
Quick Links¶
- Fact Sheet - Exam logistics, blueprint, in-scope and out-of-scope services
- Practice Plan - 8-week study schedule with checkpoints
- Scenarios - Eight worked exam-style scenarios with distractor analysis
- Strategy - Time management, trap list, exam-day tactics
- Official Exam Page - Registration and exam details
- Official Exam Guide PDF - Authoritative blueprint
- Official Exam Guide (HTML) - Same content, navigable by domain
Study Notes¶
Domain notes (weighted by exam %)¶
- Domain 1: Foundation Model Integration, Data Management, and Compliance (31%)
- Domain 2: Implementation and Integration (26%)
- Domain 3: AI Safety, Security, and Governance (20%)
- Domain 4: Operational Efficiency and Optimization for GenAI Applications (12%)
- Domain 5: Testing, Validation, and Troubleshooting (11%)
Cross-cutting deep-dives (the high-leverage topics)¶
- RAG Architecture Deep-Dive - Tested in Domains 1, 4, 5
- Agentic AI Systems - Tested in Domains 2, 5
- Bedrock Platform Deep-Dive - The single most-tested service surface
- Prompt Engineering and Management - Tested in Domains 1, 3, 5
- AWS Services Mapping (reverse index) - Last-mile service-by-service review
Companion materials in this repo (background, not Pro-level)¶
- AWS Certified AI Practitioner (AIF-C01) - Foundational cert; useful background only
- AWS Certified Machine Learning Engineer Associate (MLA-C01) - Some overlap with Bedrock and SageMaker AI
- LLMs and GenAI topic notes - Cross-cloud GenAI concepts
- Cross-cloud GenAI platform comparison
What the exam validates¶
The exam validates a candidate's ability to:
- Design and implement solutions 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.
Exam structure (one-line summary)¶
| Property | Value |
|---|---|
| Code | AIP-C01 |
| Time | 205 minutes |
| Questions | 85 total (65 scored + 10 unscored) |
| Format | Multiple choice and multiple response |
| Passing score | 750 / 1000 (scaled) |
| Cost | $300 USD |
| Validity | 3 years |
| Delivery | Pearson VUE (online proctored or test center) |
Full details in the fact sheet.
Domain weightings¶
| Domain | Weight |
|---|---|
| 1. Foundation Model Integration, Data Management, and Compliance | 31% |
| 2. Implementation and Integration | 26% |
| 3. AI Safety, Security, and Governance | 20% |
| 4. Operational Efficiency and Optimization for GenAI Applications | 12% |
| 5. Testing, Validation, and Troubleshooting | 11% |
1-week study schedule (today is 2026-05-08, target exam ~2026-05-17)¶
This schedule is weighted by domain percentage. Day 1 starts heaviest because Domain 1 is the largest (31%) and covers the foundational concepts (RAG, vector stores, prompt engineering, FM customization) that the rest of the exam builds on.
| Day | Date | Focus | Hours (target) |
|---|---|---|---|
| 1 | 2026-05-08 (Fri) | Read fact-sheet, Domain 1 tasks 1.1-1.3 (architecture, FM selection, data pipelines) | 3-4 |
| 2 | 2026-05-09 (Sat) | Domain 1 tasks 1.4-1.6 (vector stores, retrieval, prompt engineering) + RAG deep-dive | 4-5 |
| 3 | 2026-05-10 (Sun) | Domain 2 (all tasks) + Agentic AI deep-dive | 4-5 |
| 4 | 2026-05-11 (Mon) | Bedrock platform deep-dive end-to-end + Prompt engineering deep-dive | 3-4 |
| 5 | 2026-05-12 (Tue) | Domain 3 (all tasks) - guardrails, PII, governance, responsible AI | 3 |
| 6 | 2026-05-13 (Wed) | Domain 4 (cost, perf, monitoring) + Domain 5 (eval + troubleshooting) | 3 |
| 7 | 2026-05-14 (Thu) | AWS services mapping - service-by-service walkthrough; revisit weak domains | 3 |
| 8 | 2026-05-15 (Fri) | Quick-recall summaries at the bottom of every note. Light review only. | 2 |
| 9 | 2026-05-16 (Sat) | Rest day or sleep-in. Skim quick-recall summaries only. | 1 |
| - | 2026-05-17 (Sun) | Exam day | - |
Adjust the dates to your actual exam date. Total target study load: ~25-30 focused hours across 8 days.
How to read these notes¶
- Each domain note begins with the verbatim list of exam Tasks and Skills from the official guide. This is your checklist - if you understand every Skill, you can pass that domain.
- The cross-cutting deep-dives go several layers deeper than the domain notes on the highest-leverage topics. Read them after the corresponding domain note, not before.
- Every note ends with a Quick-recall summary - dense bullet list designed for last-week skimming. Treat these as your cram sheet.
- Service names use AWS short names where standard (Amazon SNS, not Amazon Simple Notification Service) - the exam itself does the same, per the official "Mentions of AWS services on the exam" page.
Prerequisites you should already have¶
Per the official target candidate description:
- 2+ years of experience building production-grade applications on AWS or with open-source technologies
- General AI/ML or data engineering experience
- 1+ year of hands-on experience implementing GenAI solutions
- Working familiarity with AWS compute, storage, networking, IAM, IaC, observability, and cost optimization
If any of these is shaky, the AIP-C01 will be brutal in 1 week. Prioritize hands-on Bedrock + Knowledge Bases + Agents work over reading.
Out of scope (don't waste study time)¶
The exam explicitly excludes these job tasks:
- Model development and training (you won't be tested on training a transformer from scratch)
- Advanced ML techniques (no math-heavy ML theory)
- Data engineering and feature engineering (no detailed Glue/EMR/Spark transformations)
A condensed list of out-of-scope AWS services (don't waste time on these): DeepRacer, DeepComposer, Forecast, Fraud Detector, Lookout family, Monitron, HealthLake, Panorama, Redshift, Timestream, Lightsail, Beanstalk, Snow Family, IoT family, Alexa for Business, GameLift, Braket. Full list in the fact sheet.