AWSAWS Certified Generative AI Developer - Professional
The AWS Certified Generative AI Developer - Professional certification validates your ability to design, implement, and deploy generative AI solutions on AWS. It is for developers and AI practitioners who build production-grade applications using foundation models, RAG, and agentic AI. Earning it demonstrates advanced, hands-on expertise in integrating FMs into real-world business workflows.
230 practice questions · Updated 2026-08-07
2Domains
9Objectives
72Concepts
230Questions
AIP-C01 Curriculum
Every domain, objective, and concept the AIP-C01 exam measures.
- Business and technical requirements analysis
- Foundation model selection criteria
- Integration patterns for GenAI
- Deployment strategies for GenAI
- Architectural design documentation
- Proof-of-concept planning
- POC implementation with Amazon Bedrock
- POC evaluation and decision-making
- Standardized component design
- AWS Well-Architected Framework application
- Generative AI Lens for Well-Architected
- FM selection criteria
- Dynamic model selection architecture
- Resilient AI system design
- FM customization deployment
- Model versioning and lifecycle management
- Data quality validation workflows
- Handling complex data types for FM
- Model-specific input formatting
- Input data quality enhancement
- Advanced Vector Database Architectures for FM Augmentation
- Hierarchical and Topic-Based Segmentation
- Metadata Frameworks for Search Precision
- High-Performance Vector Database Optimization
- AWS Integration Components for Data Sources
- Data Maintenance Systems for Vector Stores
- Document segmentation strategies
- Embedding model selection
- Batch embedding generation
- Vector store deployment
- Advanced search architectures
- Query expansion and decomposition
- Retrieval integration interfaces
- Model Instruction Frameworks
- Interactive AI Systems for Context
- Prompt Management and Governance
- Quality Assurance for Prompts
- Iterative Prompt Enhancement
- Complex Prompt Systems Design
- Memory and State Management in Agentic Systems
- MCP for Agent-Tool Interactions
- ReAct Pattern Implementation
- Chain-of-Thought Reasoning Approaches
- Stopping Conditions in AI Workflows
- Timeout Mechanisms with Lambda
- IAM Policies for Resource Boundaries
- Circuit Breakers for Failure Mitigation
- Specialized FM Selection for Complex Tasks
- Custom Aggregation Logic for Model Ensembles
- Model Selection Frameworks
- Orchestrating Human Review and Approval
- Feedback Collection with API Gateway
- Human Augmentation Patterns
- Custom Behaviors via Strands API
- Standardized Function Definitions
- Error Handling and Parameter Validation in Lambda
- Stateless MCP Servers with Lambda
- Complex MCP Servers with Amazon ECS
- MCP Client Libraries for Consistent Access
- On-demand invocation with Lambda
- Provisioned throughput in Amazon Bedrock
- SageMaker AI endpoints for hybrid solutions
- Selecting deployment based on application needs
- Container-based deployment patterns for LLMs
- Specialized model loading strategies
- Balancing performance and resource requirements
- Using smaller pre-trained models for specific tasks
- API-based model cascading
- API-based integration with legacy systems
- Event-driven architecture for loose coupling
- Data synchronization patterns
- Enterprise integration patterns for FM
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Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for AIP-C01, so none is invented.