AWS Certified Generative AI Developer - Professional (AIP-C01) Domain 2

Implementation and Integration

Official Exam Guide: Domain 2: Implementation and Integration

Skill Builder: AWS Certified Generative AI Developer - Professional Exam Prep

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Domain Overview

Domain 2 (26% of exam) focuses on implementing agentic AI solutions, model deployment strategies, enterprise integration architectures, FM API integrations, and application integration patterns.

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Task 2.1: Implement agentic AI solutions and tool integrations

Key Skills:

• Develop intelligent autonomous systems with memory and state management
• Create advanced problem-solving systems (ReAct patterns, chain-of-thought)
• Develop safeguarded AI workflows with controls
• Create model coordination systems (ensembles, routing)
• Develop collaborative AI systems (human-in-the-loop)
• Implement intelligent tool integrations and MCP servers

Essential Documentation:

• Amazon Bedrock Agents
• AWS Step Functions
• Model Context Protocol (MCP)
• Amazon Bedrock Agent Action Groups

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Task 2.2: Implement model deployment strategies

Key Skills:

• Deploy FMs based on application needs (on-demand, provisioned throughput)
• Deploy solutions addressing LLM-specific challenges
• Develop optimized deployment approaches (model cascading, smaller models)

Essential Documentation:

• Amazon Bedrock Provisioned Throughput
• SageMaker Model Deployment
• AWS Lambda

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Task 2.3: Design and implement enterprise integration architectures

Key Skills:

• Create enterprise connectivity solutions
• Develop integrated AI capabilities for existing applications
• Create secure access frameworks
• Develop cross-environment AI solutions
• Implement CI/CD pipelines for GenAI

Essential Documentation:

• Amazon API Gateway
• Amazon EventBridge
• AWS CodePipeline
• AWS Outposts

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Task 2.4: Implement FM API integrations

Key Skills:

• Create flexible model interaction systems (synchronous/asynchronous)
• Develop real-time AI interaction systems (streaming APIs)
• Create resilient FM systems (exponential backoff, rate limiting, fallback)
• Develop intelligent model routing systems

Essential Documentation:

• Amazon Bedrock Runtime API
• Invoke Foundation Models
• Streaming Responses
• AWS X-Ray

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Task 2.5: Implement application integration patterns and development tools

Key Skills:

• Create FM API interfaces for GenAI workloads
• Develop accessible AI interfaces (no-code, low-code)
• Create business system enhancements
• Enhance developer productivity (Amazon Q Developer)
• Develop advanced GenAI applications
• Improve troubleshooting efficiency

Essential Documentation:

• AWS Amplify
• Amazon Bedrock Prompt Flows
• Amazon Q Developer
• Amazon Q Business
• Amazon Bedrock Data Automation

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AWS Service FAQs

• Amazon Bedrock FAQs
• AWS Step Functions FAQs
• Amazon API Gateway FAQs
• AWS Lambda FAQs

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Study Tips

1. Master Amazon Bedrock Agents - Agents are central to agentic AI. Understand action groups, knowledge bases, tool integration, and multi-agent orchestration.

2. Practice MCP implementations - Model Context Protocol enables extensible tool integrations. Learn to build MCP servers and clients.

3. Learn deployment patterns - Understand when to use provisioned throughput vs on-demand, Lambda vs containers, and model cascading for cost optimization.

4. Study streaming implementations - Real-time streaming is critical for user experience. Master streaming APIs and incremental response handling.

5. Understand enterprise integration - Learn API Gateway patterns, EventBridge for event-driven architectures, and secure multi-environment deployments.

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Note: This is Domain 2 of 5, representing 26% of exam content.