Ultimate AWS Certified Generative AI Developer Professional
Hands-on GenAI development on AWS with Bedrock, SageMaker, and Flows - includes 2 complete 75-question practice exams
Hands-on GenAI development on AWS with Bedrock, SageMaker, and Flows - includes 2 complete 75-question practice exams
This course contains the use of artificial intelligence for drafting assessment questions. No AI avatars or synthetic voices are used.
Become an AWS-certified generative AI professional with the most comprehensive preparation available for the AWS Certified Generative AI Developer – Professional (AIP-C01 / AP1-C01) exam. This course gives you everything you need—hands-on practice, deep technical coverage, a full practice exam, and complete alignment with the official exam guide.
What’s Included
24+ hours of self-paced video training across 300+ lectures
TWO full-length 75-question practice exams to benchmark your readiness
50+ hands-on activities and labs to build real skills
Downloadable PDFs for all slide decks and a study guide
120 section-level quiz questions written in AWS style
This is a challenging professional-level certification - many say THE most challenging. If you are new to AWS certifications, we strongly recommend starting with a practitioner or associate level one, such as the AI Practitioner exam, to get a feel for how AWS exams work. This should not be your first AWS exam.
What You’ll Learn
Generative AI on AWS blends the world’s most popular cloud platform with cutting-edge AI techniques. This course helps you master both—through real scenarios, guided examples, and practical architecture patterns. You’ll learn how to:
Build GenAI applications using Amazon Bedrock, SageMaker, and Knowledge Bases
Design agentic AI systems using Bedrock, Flows, OpenSearch, S3 Vectors, Strands, Agent Squad, and AgentCore
Apply optimization techniques for RAG pipelines, embedding strategies, and foundation model performance
Manage prompts and structured workflows using Prompt Management and Bedrock Flows
Evaluate model quality and safety using Bedrock Evaluations
Prepare and process data with Bedrock Data Automation, SageMaker, Glue, Comprehend, Textract, and more
Orchestrate production-grade AI systems using Step Functions, Lambda, Pipelines, and CI/CD tools
Every module is grounded in the AWS exam domains and the skills described in AWS’s official documentation. We’ve mapped the entire exam blueprint to ensure you don’t miss a thing.
Meet Your Instructors
Frank Kane (Sundog Education)
Frank has guided over one million learners on Udemy across AI, machine learning, and data engineering. He's taken and passed the AIP-C01 exam himself, and ensured this course comprehensively covers its contents. Frank spent nine years at Amazon headquarters as a senior engineer and senior manager, building large-scale machine learning systems and earning 26 issued patents. Frank specializes in making complex AI topics practical and approachable.
Stéphane Maarek
Stéphane is one of the most trusted AWS educators in the world, with over three million learners on Udemy and deep expertise across AWS’s certification tracks.
If you're ready to master generative AI on AWS and earn one of the most forward-looking certifications available, we’re excited to guide you every step of the way. Let’s get started!
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Section Intro
89 Xem trướcAmazon Bedrock Overview
484 Xem trướcA quick note on model access
Xem trướcHands-On with the Bedrock Playground
762More Depth on the Bedrock Converse API
Fine-Tuning Foundation Models in Bedrock
457Low-Rank Adaptation (LoRA) - How Fine-Tuning Works
176Retrieval-Augmented Generation (RAG)
508Vector Stores and Semantic Search
483Bedrock Knowledge Bases
301Hands-On with Knowledge Bases
966Really, don't leave OpenSearch up and running for long!
Pre-Retrieval and Chunking Strategies
288Managing Chunking Strategies with Bedrock
408Optimizing your Vector Store and Embeddings
492Evaluating RAG Performance
269Mulitmodal Models and Pipelines with Bedrock
209Bedrock Guardrails
225Bedrock Guardrails Automated Reasoning Checks
111Hands-On with Bedrock Guardrails
582Token-Level Redaction
130Amazon Bedrock Prompt Management
306Bedrock Prompt Flows
295Enforcing Use of Structured Data
215Intro to Prompt Engineering
211Anatomy of a Prompt
136Prompt Best Practices
253Types of Prompts
194Prompt Misuse and Mitigating Bias
519Enterprise Integration
205AWS Well-Architected Tool Generative AI Lens
162Quiz: Bedrock and GenAI Fundamentals
Section Intro
116Dealing with Structured Data
409Amazon Bedrock Data Automation
675SageMaker Data Wrangler
414NOTICE: Data Wrangler can be hard to find
Demo: SageMaker Studio, Canvas, and Data Wrangler
1460AWS Glue
363AWS Glue Studio
319Glue Data Quality
177CloudWatch Metrics
249AWS Transcribe
259AWS Transcribe - Hands On
104Amazon Comprehend
213Amazon Comprehend - Hands On
266Using Comprehend, Lambda, and Bedrock together
82Amazon Comprehend Medical
95Intro to OpenSearch in GenAI
Introducing Amazon OpenSearch Service (part 1)
685Introducing Amazon OpenSearch Service (part 2)
443OpenSearch Index Management and Designing for Stability
654Amazon OpenSearch Service Performance
89Amazon OpenSearch Serverless
120Using and Tuning OpenSearch as a Vector Store
569Amazon S3 Vectors
524Amazon S3 Vectors - Hands On
612Note on the following supplemental video
S3 Vectors with Claude Code and Agent Toolkit for AWS [BONUS - NOT ON EXAM]
896Amazon RDS
218Amazon RDS - Hands On
626Amazon RDS with S3 Document Repositories
78Amazon Aurora
387Amazon Aurora - Hands On
502Amazon Aurora and the pgvector Extension
103Amazon DynamoDB
467Amazon DynamoDB - Hands On
522Amazon DynamoDB - WCU & RCU
665Amazon DynamoDB - WCU & RCU - Hands On
246Amazon DynamoDB - Basic APIs
474Amazon DynamoDB - Basic APIs - Hands On
190Amazon DynamoDB DAX
165Amazon DynamoDB DAX - Hands On
248Amazon DynamoDB - TTL
320DynamoDB and Generative AI
423Keeping your Vector Store Up to Date
143Re-Ranker Modules in Bedrock
93Amazon S3 - Storage Classes
372Amazon S3 - Storage Classes - Hands On
217Amazon S3 - Lifecycle Rules
260Amazon S3 - Lifecycle Rules - Hands On
144A note about S3 regional namespaces
Amazon S3 - Replication
85Amazon S3 - Replication - Notes
57Amazon S3 - Replication - Hands On
389S3 Encryption
485About DSSE-KMS
S3 Encryption - Hands On
294S3 Default Encryption
80S3 Access Logs
76S3 Access Logs - Hands On
212S3 Access Points
214Quiz: Managing Data for Generative AI
Section Intro
114NOTE: Amazon is deprecating Bedrock Agents
LLM Agents in Bedrock
600Hands On: Amazon Bedrock Agents Classic
1214WARNING: Remember to delete your OpenSearch serverless resources!
Multi-Agent Workflows
537Short and Long-Term Agent Memory
218Strands Agents
333Agent Squad
175Amazon AgentCore Introduction
401AgentCore Memory and Tools
215AgentCore Bedrock Import, Gateway, and Identity
217AgentCore Policies
546AgentCore Evaluators
732Note on the following lab for EU users
Lab: Strands Agents, Amazon Bedrock AgentCore, Agent Squad
1956Lab: Amazon Bedrock AgentCore Harnesses
383Model Context Protocol (MCP)
283OpenAPI and Tool Usage
92Humans in the Loop
165Amazon Q Apps
53Amazon Q Apps - Hands On
208Introducing Amazon Quick
438How Amazon Quick Works: Chat and Agents
304Grounding Amazon Quick in Your Data (Spaces, Knowledge, Index, Connectors)
331Automating Work with Amazon Quick Flows and Quick Automate
296Producing Deliverables with Amazon Quick: Research and Apps
228Running Amazon Quick: Security, Pricing, and Limits
296Amazon Quick - Hands-On
916Quiz: Agentic AI
Section Intro
115Token Efficiency
379Cost-Effective Model Selection
290Maximizing Resource Utilization and Throughput
286Intelligent Caching Systems for GenAI
464Building Responsive AI Systems
296Optimizing Retrieval Performance
219Optimizing for Specific Use Cases
158Optimizing Foundation Model System Performance
276Exponential Backoff and Connection Pooling
183Amazon Bedrock Cross-Region Inference
281Quiz: Operational Efficiency and Optimization
Section Intro
101Intro to Amazon SageMaker AI
192Data Processing, Training, and Deployment with SageMaker
334SageMaker Deployment Safeguards
378Optimizing Foundation Model Deployments
356NOTE: Amazon is deprecating SageMaker Ground Truth, Model Monitor, and Clarify
SageMaker Ground Truth
335SageMaker Model Monitor and Clarify
274SageMaker Model Registry
187SageMaker Lineage Tracking
211Cross-Account Lineage Tracking
34SageMaker on the Edge (Neo)
258SageMaker Unified Studio
180SageMaker Pipelines
117Hands On: SageMaker JumpStart
891Quiz: Managing Models with SageMaker AI
Section Intro
128AWS Lambda
289Lambda Integration Patterns, Part 1
309Lambda Integration Patterns, Part 2
404Lambda with Bedrock
123Amazon API Gateway
397Amazon API Gateway - Hands On
619Amazon API Gateway and Generative AI Applications
146AWS AppConfig
164Dynamic FM Selection with AppConfig
77AWS Step Functions
235Step Function States
199Step Functions Circuit Breaker Pattern, and AI Integration
263Lab: Prompt Chaining with Step Functions and Bedrock
590AWS CodePipeline
236AWS CodePipeline - Hands On - Prerequisites
127AWS CodePipeline - Hands On
614AWS CodeBuild
258AWS CodeBuild - Hands On - Part 1
248AWS CodeBuild - Hands On - Part 2
569AWS CodeDeploy
495AWS CodeDeploy - Hands On
735MLFlow for LLM Experimentation
108AWS AppSync and GenAI
104AWS Outposts
158AWS Outposts and GenAI
96AWS Wavelength
116AWS Wavelength and GenAI
69Amazon SQS
633Amazon SQS - Hands On
387AWS Amplify
119Amazon EventBridge
419Amazon EventBridge - Hands On
411Amazon SNS
258Amazon SNS - Hands On
275Amazon AppFlow
83Quiz: More Tools for Building AI Applications
Section Intro
124Bedrock Agent Tracing
143Evaluation Techniques for Foundation Models
391Evaluating LLM's with ROUGE, BLUE, and BERT scores
310Amazon Bedrock Model Evaluations
223Deployment Validation Systems
159Principles of Responsible AI
401CloudWatch Logs
361CloudWatch Logs - Hands On
309CloudWatch Alarms
253CloudWatch Alarms - Hands On
278CloudWatch RUM
65CloudWatch and GenAI Monitoring
153AWS CloudTrail
342AWS CloudTrail - Hands On
90CloudTrail and GenAI
47Clarification on CloudTrail + Bedrock
AWS X-Ray
427AWS X-Ray - Hands On
493AWS Lake Formation
394Quiz: Governance and QA
Section Intro
116Principle of Least Privilege
126Data Masking and Key Salting
152IAM Introduction: Users, Groups, Policies
202IAM Users & Groups - Hands On
383AWS Console Simultaneous Sign-in
108IAM Policies
170IAM Policies - Hands On
481IAM MFA
250IAM MFA - Hands On
241IAM Roles
99IAM Roles - Hands On
125AWS IAM Identity Center
405AWS Control Tower
168Encryption 101
239AWS KMS
448AWS KMS - Hands On
571Amazon Macie
63AWS Secrets Manager
130AWS Secrets Manager - Hands On
240Amazon Cognito
392AWS WAF
181VPC, Subnets, Internet Gateway, NAT Gateway
323NACL, Security Groups, VPC Flow Logs
279VPC Peering, Endpoints, VPN, Direct Connect
329VPC Cheat Sheet & Closing Comments
154AWS PrivateLink
124Quiz: Security, Identity, and Compliance
AWS CDK
291AWS CDK - Hands On
693AWS Access Keys, CLI and SDK
243AWS CLI Setup on Windows
105AWS CLI Setup on Mac
88AWS CLI Setup on Linux
90AWS CLI - Hands On
230AWS CloudFormation
211AWS CloudFormation - Hands On
512AWS CodeArtifact
361AWS CodeArtifact - Hands On
384Quiz: Developer Tools
NOTE: Amazon is deprecating Amazon Augmented AI and Amazon Kendra
Amazon Augmented AI
116Amazon Augmented AI - Hands On
161Amazon Kendra
82Amazon Lex
112Amazon Lex - Hands On
274Amazon Rekognition
340Amazon Rekognition - Hands On
162Amazon Textract
56Amazon Textract - Hands On
201Quiz: Machine Learning
Intro: Getting Ready for the Exam
73More Preparation Resources and the Exam Guide
603Additional Prep from the Community
Test-Taking Tips
496Exam Walkthrough and Signup
277Save 50% on your AWS Exam Cost!
70Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers Only
64Congratulations!
88Bonus Lecture
Basic AWS knowledge is recommended. You should be comfortable with core AWS services such as IAM, S3, Lambda, and VPCs.
Familiarity with cloud or software development concepts (APIs, JSON, serverless workflows) will help you move faster through hands-on sections.
Some exposure to AI or machine learning is useful, but no deep ML, data science, or model-training background is required—we cover all generative AI concepts from the ground up.
An AWS account is needed to complete the hands-on labs. Free-tier usage is sufficient for most exercises.
A computer and stable internet connection are required to follow along with the video lessons and practical activities.
No prior AWS certification is required, but experience with associate-level AWS material will make the Professional exam topics more approachable.
Master the skills required to pass the AWS Generative AI Developer Professional certification exam.
Build production-ready generative AI apps on AWS using Bedrock, SageMaker, and serverless tools.
Design and optimize RAG pipelines with embeddings, vector databases, and retrieval tuning.
Create agentic AI workflows using Bedrock Agents, Flows, tools, and multi-agent patterns.
Evaluate and improve model quality with Bedrock Evaluations, grounding checks, and safety controls.
Automate and scale GenAI systems with Step Functions, Lambda, CI/CD, and AWS best practices.