AWS Certified Generative AI Developer

Expert Level

Build and validate the advanced AWS skills needed to take generative AI applications from design to production. 

The AWS Certified Generative AI Developer – Professional credential is designed for experienced developers who integrate foundation models into applications and business workflows. The certification validates the ability to build secure, scalable and responsible generative AI solutions using AWS technologies.

It covers foundation model integration, Retrieval Augmented Generation (RAG), vector stores, prompt management, agentic AI, application security, governance, monitoring and cost optimisation.

Ready to prove that you can build production-ready generative AI applications on AWS?

Build generative AI applications that are ready for production.

The 3-days Advanced Generative AI Development on AWS course develops the practical skills needed to design, integrate and operate advanced generative AI systems.

You will learn how to:

  • Evaluate and select foundation models for specific business requirements
  • Integrate foundation models into applications and enterprise workflows
  • Build RAG solutions using Amazon Bedrock Knowledge Bases and vector databases
  • Develop agentic AI solutions with Amazon Bedrock AgentCore
  • Establish advanced prompt-engineering and prompt-governance frameworks
  • Apply security, privacy, responsible AI and compliance controls
  • Improve application performance, reliability and cost efficiency
  • Monitor, test, validate and troubleshoot generative AI applications
  • Integrate generative AI securely into existing enterprise environments

The course uses architectural examples, hands-on labs and implementation scenarios to help you move beyond experimentation and build systems that can operate reliably at scale.

Training Fees:

RM5,400/pax RM4,500

Certification Fee:

RM675
RM338
(50% Promo till end of 2026)

Intakes:

16-18 Nov 2026

What are the key skills measured:

  • Foundation model integration, data management and compliance: 31%

  • Implementation and integration: 26% 

  • AI safety, security and governance: 20% 

  • Operational efficiency and optimization: 12% 

  • Testing, validation and troubleshooting: 11% 

Who is this for?

  • Generative AI developers 

  • Software developers 

  • AI and machine learning engineers 

  • Cloud application developers 

  • Cloud engineers 

  • Solutions architects involved in generative AI delivery 

  • Technical leads responsible for production AI applications 

  • Experienced data or AI practitioners moving into generative AI development 

Production-ready generative AI skills are becoming a business priority

78%

78% of organizations reported using AI in 2024, up from 55% the previous year, according to Stanford HAI’s 2025 AI Index. This growth increases the need for professionals who can move AI applications beyond prototypes and operate them responsibly in production.
Source: Stanford HAI, 2025 AI Index Report

52%

52% of organizations rated their data foundations as inadequate for generative AI implementation. Developers need strong data integration, retrieval and governance skills to build dependable enterprise applications. 
Source: AWS CDO Agenda 2025

47%

47% of executives said their organizations were developing and releasing generative AI tools too slowly. Among those executives, 46% identified talent skill gaps as the main cause.
Source: McKinsey, AI in the Workplace 2025

Prove production-level generative AI expertise

Demonstrate that you can integrate foundation models, RAG, vector stores and agents into applications that meet enterprise requirements.

Address security and governance from the start

Validate your ability to apply identity controls, privacy safeguards, responsible AI practices, testing and governance throughout the application lifecycle.

Strengthen your value in technical AI projects

Show employers and clients that you can connect generative AI concepts with application development, cloud architecture and operational delivery.

Progress beyond AI experimentation

Develop the skills to improve application quality, reliability, observability, performance and cost efficiency after deployment.

Why choose Trainocate?

Trainocate delivers official AWS training through experienced instructors and practical, vendor-aligned courseware. Its recognition as an AWS Training Partner of the Year from 2022 to 2025 reflects its track record in developing AWS cloud and AI capabilities.

Exam Overview

Exam Code:

AIP-C01

Category:

Professional

Exam Duration:

180 minutes

Exam Format:

75 multiple-choice or multiple-response questions

Passing score:

750 out of 1,000

Cost:

USD300
*Subject to applicable taxes and exchange rates

Language offered:

English, Japanese, Korean and Simplified Chinese

Testing Options:

Pearson VUE testing centre or online-proctored exam

Certification validity:

Three years

Open up new possibilities for your career

Frequently Asked Questions (FAQs)

It is a professional-level AWS certification that validates the ability to integrate foundation models into production applications and business workflows.

The certification covers RAG, vector stores, prompt management, agentic AI, application integration, responsible AI, security, monitoring and optimisation. It focuses on implementing generative AI solutions rather than training foundation models.

Pro Tip: Review the five AIP-C01 exam domains before planning your preparation so you can identify where you need additional hands-on practice.

The exam is intended for experienced developers and technical professionals who already build applications and have implemented generative AI solutions.

AWS recommends at least two years of production application development experience and one year of hands-on generative AI implementation experience. Candidates should also understand core AWS infrastructure, security, deployment and monitoring services.

Pro Tip: If you have limited AWS or generative AI experience, complete AWS Technical Essentials and Generative AI Essentials on AWS before attempting advanced training.

The exam covers foundation model integration, RAG, agentic AI, security, governance, operational optimisation, testing and troubleshooting.

The largest domain is Foundation Model Integration, Data Management and Compliance at 31%, followed by Implementation and Integration at 26%.

Pro Tip: Give additional preparation time to the first three domains because together they account for 77% of the scored exam content.

No. The exam focuses on integrating and operating foundation models rather than developing or training machine learning models.

Advanced machine learning methods, model training, feature engineering and extensive data engineering are outside the exam’s intended scope.

Pro Tip: Focus your preparation on application architecture, Amazon Bedrock, retrieval, agents, security and operational delivery instead of model-training mathematics.

Candidates should understand the AWS services used to build, secure, deploy and monitor generative AI applications.

Relevant technologies include Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Bedrock AgentCore, Amazon OpenSearch Service, AWS Identity and Access Management, Amazon CloudWatch and supporting compute, storage, networking and deployment services.

Pro Tip: Practise connecting these services in an end-to-end application rather than studying each service in isolation.

The three-day course develops hands-on skills across the generative AI application lifecycle, from foundation model selection to enterprise integration and monitoring.

It covers advanced RAG, vector databases, prompt governance, agentic AI, security controls, performance optimisation, observability, testing and production architecture. The certification exam is available separately.

Pro Tip: Use the course labs to document an architecture you can revisit during exam preparation and discuss in technical interviews.

Yes, for experienced developers responsible for turning generative AI concepts into secure, scalable and measurable applications.

The credential is especially relevant as organisations move from isolated pilots to production systems and need professionals who understand integration, security, governance, reliability and cost.

Pro Tip: Pair the certification with a working RAG or agentic AI project to demonstrate both validated knowledge and practical delivery experience.

It is relevant to generative AI developer, AI application developer, cloud developer, AI engineer and technical lead roles.

It can also support solutions architects and experienced cloud professionals whose responsibilities include designing or governing generative AI applications. Certification alone does not replace the production experience AWS expects from candidates.

Pro Tip: Match your portfolio to your target role by showing how you handled retrieval quality, security, monitoring, latency and cost in a real application.

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