AWS Certified Generative AI Developer Professional (AIP-C01) | Udemy

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.

• Laser-focused on designing and building production grade, enterprise ready GenAI and Agentic AI architecture
• 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 (Carefully crafted to test each concept)
• 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

Architecting Retrieval Augmented Generation (RAG) Systems

You’ll design end-to-end RAG pipelines and understand the trade-offs behind each decision, including:

• Selecting and preparing data sources for retrieval-based architectures
• Choosing chunking strategies, embeddings, and vector databases based on recall, precision, and scale
• Optimizing retrieval using reranking, hybrid search, and evaluation techniques
• Implementing Amazon Bedrock Knowledge Bases for managed RAG solutions

Building Agentic AI Systems on AWS

You’ll move beyond single-prompt LLM applications and design agent-based systems, including:

• Architecting workflows with Amazon Bedrock Agents
• Integrating tools and context using the Model Context Protocol (MCP)
• Deploying and operating agents using Amazon Bedrock AgentCore

Who This Course Is For

– AWS developers preparing for the AIP-C01 certification

– Cloud architects adding generative AI to their skillset

– Busy professionals who want focused, efficient exam preparation

– Engineers who learn better through architecture reasoning than feature memorization

Requirements

• Basic AWS knowledge (IAM, S3, Lambda)
• Familiarity with REST APIs and JSON
• AWS account for hands-on labs (Free Tier eligible)
• No prior AI/ML experience required