Udemy – Udemy – AWS Certified Generative AI Developer Professional (AIP-C01)
English | Tutorial | Size: 6.1 GB
Production-Grade Architecture, Hands-On Exam Prep: Amazon Bedrock, RAG & AI Agents | 40+ Labs | 60+ Exam Scenarios
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
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