New AWS Certification: Generative AI Developer Professional

New AWS Certified Generative AI Developer – Professional: What You Need to Know

AWS has launched its most ambitious certification yet: the AWS Certified Generative AI Developer – Professional. This certification validates your ability to build production-ready AI applications using AWS services, and it’s positioned as the premier credential for developers entering the generative AI space.

Currently in beta with limited slots available, the first 5,000 successful candidates receive an exclusive Early Adopter badge, adding additional value for those who move quickly. This comprehensive guide covers everything you need to know to prepare for and pass this challenging exam.

Certification Overview and Requirements

Exam Domains and Topics

The certification covers four major domains, each testing different aspects of building generative AI applications on AWS:

Domain 1: Foundation Models and Prompt Engineering (30%)

This is the largest domain, reflecting the importance of understanding how to work with large language models effectively:

• Selecting appropriate foundation models for specific use cases (Claude, Llama, Titan, Mistral)
• Advanced prompt engineering techniques including chain-of-thought, few-shot learning, and system prompts
• Fine-tuning foundation models with custom datasets using SageMaker
• Model evaluation metrics, benchmarking approaches, and A/B testing
• Understanding model capabilities, limitations, context windows, and trade-offs
• Token optimization and cost management for production deployments
• Model versioning and lifecycle management

Domain 2: RAG Architectures and Knowledge Bases (25%)

Retrieval-Augmented Generation is a critical pattern for enterprise AI applications that need to access proprietary data:

• Designing vector database architectures using OpenSearch Serverless, Amazon Aurora pgvector, and third-party solutions
• Embedding model selection (Amazon Titan Embeddings, Cohere) and implementation strategies
• Chunking strategies for different document types (PDF, HTML, structured data)
• Knowledge base integration with Amazon Bedrock for automated RAG pipelines
• Retrieval optimization techniques including hybrid search and reranking
• Handling structured and unstructured data sources at scale
• Data synchronization and refresh strategies for knowledge bases

Domain 3: Application Development and Integration (25%)

Building production applications requires deep integration knowledge across the AWS ecosystem:

• Amazon Bedrock APIs and SDKs for Python, JavaScript, and Java
• LangChain and LlamaIndex integration patterns with AWS services
• Building and orchestrating AI agents using Bedrock Agents
• Tool use and function calling implementations for extended capabilities
• Streaming responses and real-time applications with WebSockets
• Conversation memory and session management patterns
• Multi-modal applications handling text, images, and documents
• Error handling, retry logic, and production resilience

Domain 4: Security, Compliance, and Responsible AI (20%)

Enterprise AI requires robust governance and safety measures that this domain thoroughly covers:

• Implementing Amazon Bedrock Guardrails for content filtering and topic avoidance
• Data privacy and compliance frameworks (HIPAA, SOC2, GDPR, FedRAMP)
• Hallucination detection and prevention strategies
• PII detection and redaction in prompts and responses
• Cost optimization, usage monitoring, and budget controls
• Audit logging with CloudTrail and governance controls
• Model access permissions and cross-account sharing

AWS AI/ML Services You Must Know

The exam tests deep knowledge of these AWS services and their integration patterns:

# Core services for the exam:
Amazon Bedrock
  - Foundation models (Claude, Llama, Titan, Mistral, Cohere)
  - Bedrock Agents and action groups
  - Knowledge Bases for RAG
  - Guardrails for content safety
  - Custom model import and fine-tuning
  - Provisioned throughput for production

Amazon SageMaker
  - JumpStart for model deployment
  - Fine-tuning with custom datasets
  - Model hosting and endpoints
  - Feature Store for embeddings

Vector Databases
  - Amazon OpenSearch Serverless
  - Amazon Aurora PostgreSQL (pgvector)
  - Amazon Kendra for enterprise search

Supporting Services
  - AWS Lambda for agent functions
  - Amazon S3 for knowledge base data
  - Amazon CloudWatch for monitoring
  - AWS IAM for access control
  - AWS Secrets Manager for API keys

Generative AI Skills Covered

Beyond AWS service knowledge, the certification validates practical generative AI development skills:

• Prompt Engineering: Writing effective system prompts, handling edge cases, optimizing for specific outputs
• RAG Implementation: Building end-to-end retrieval systems from document ingestion to response generation
• Agent Development: Creating autonomous AI agents that can use tools and make decisions
• Evaluation: Measuring model quality, relevance, and safety in production
• Optimization: Reducing latency, managing costs, and scaling applications
• Safety: Implementing guardrails, detecting harmful content, preventing misuse

Target Audience and Roles

This certification is designed for professionals in these roles who want to validate their AI development expertise:

• AI/ML Engineers building generative AI applications in production environments
• Software Developers integrating LLMs into existing applications and platforms
• Solutions Architects designing AI-powered systems for enterprise clients
• Data Scientists productionizing AI models and building RAG systems
• Technical Leads guiding teams in AI adoption and best practices

Study Resources and Preparation Path

Official AWS Resources

1. AWS Skill Builder: Generative AI learning path with 20+ hours of content and hands-on labs
2. Amazon Bedrock Workshop: Official AWS workshop with step-by-step exercises covering all exam domains
3. AWS Documentation: Bedrock developer guide, API reference, and best practices guides
4. AWS re:Invent Sessions: Deep-dive talks on generative AI architecture from re:Invent 2024 and 2025
5. AWS Blogs: Machine Learning blog posts on Bedrock patterns and implementations

Recommended Hands-On Labs (Essential)

Hands-on experience is critical for this exam. Complete at least these five labs:

• Build a complete RAG application with Bedrock Knowledge Bases and OpenSearch
• Create a Bedrock Agent with custom tools and action groups
• Implement Guardrails for content moderation and PII filtering
• Fine-tune a model using SageMaker JumpStart with custom data
• Deploy a streaming chat application with conversation memory

How It Fits in AWS Certification Path

The Generative AI Developer Professional is positioned at the highest certification level, alongside other professional certifications:

Also New: AWS Agentic AI Microcredential

AWS also launched a focused microcredential specifically for building AI agents. While the Professional certification validates broad generative AI knowledge, the microcredential demonstrates hands-on expertise in autonomous AI systems:

• Designing multi-step agent workflows with decision trees
• Implementing tool use and function calling with external APIs
• Managing agent memory, context, and conversation state
• Orchestrating agent-to-agent communication for complex tasks
• Testing and evaluating agent behavior and reliability

Consider pursuing both credentials for maximum career impact in AI development roles.

Getting Started: Your 8-Week Preparation Plan

Follow this structured approach to prepare for the exam:

1. Week 1-2: Complete AWS Skill Builder Generative AI learning path and enable Amazon Bedrock in your AWS account
2. Week 3-4: Build your first RAG application using Bedrock Knowledge Bases with a sample document set
3. Week 5: Create a Bedrock Agent with custom tools and practice prompt engineering
4. Week 6: Implement Guardrails and study security/compliance requirements
5. Week 7: Complete additional hands-on labs and review all exam domains
6. Week 8: Take practice exams, review weak areas, and schedule your exam

Register for the beta exam early to secure your opportunity for the Early Adopter badge. With demand for AI skills continuing to grow, this certification positions you at the forefront of the generative AI revolution.