A Different Approach to GenAI Certification Prep. The AWS Certified Generative AI Developer – Professional (AIP-C01) exam is a tricky, scenario-based certification. It’s not about bullet-point memorization. It tests your ability to make architecture decisions and trade-offs across real-world generative AI systems.
That is exactly how this course is designed.
- 10 hours. Laser-focused.
- Built around how the exam actually tests you — scenarios, trade-offs, and multi-layered architecture decisions.
- I designed this course after passing the AIP-C01 exam myself, intentionally moving away from 20+ hour “comprehensive” courses that prioritize coverage over clarity.
What Makes This Course Different:
- Architecture-First Learning – Every concept is taught through the lens of production design decisions, not isolated feature walkthroughs
- Two Complete Projects – Build a GenAI Equipment SME Assistant (15 architecture decisions) and a RAG-powered E-Learning Q&A system (10 architecture decisions)
- Exam-Realistic Practice – 40+ scenario-based quiz questions + mini mock exam written in AWS exam style. Quiz questions at Professional-level difficulty focused on decision-making, not recall. Quality over quantity.
- Hands-On Mastery – 40+ demos of the most complex concepts, so you learn by building, not by watching slides
- Respects Your Time – 10 hours of focused content. No filler. No irrelevant lectures.
Course Contents:
- 10 hours of focused video content
- 40+ hands-on demos covering complex, real-world scenarios
- 40+ exam-style questions across topic quizzes
- Mini mock exam to test your readiness
- 300+ structured slides aligned to architecture decisions
- 2 complete projects with production architecture walkthroughs
- Backed by Udemy’s 30-day money-back guarantee.
What You’ll Be Able to Design, Evaluate, and Decide
This course prepares you to think and decide like the AWS exam expects — by evaluating trade-offs across real-world generative AI architectures.
Designing GenAI Applications with Amazon Bedrock
You’ll learn how to make the right architectural choices when building GenAI applications on AWS, including:
- Choosing the right foundation model based on accuracy, latency, cost, and use case constraints
- Tuning inference parameters, provisioned throughput, and capacity for production workloads
- Applying guardrails, content filters, and Responsible AI controls to meet safety and compliance requirements
- Observability and Monitoring
- Model customization: distillation, fine-tuning, and continued pre-training
- Model evaluation using programmatic metrics, LLM-as-a-Judge, and human review
