Content Domain 2: Implementation and Integration
Task 2.1: Implement agentic AI solutions and tool integrations.
Skill 2.1.1: Develop intelligent autonomous systems with appropriate memory and state management capabilities (for example, by using Strands Agents and AWS Agent Squad for multi-agent systems, MCP for agent-tool interactions).
Skill 2.1.2: Create advanced problem-solving systems to give FMs the ability to break down and solve complex problems by following structured reasoning steps (for example, by using Step Functions to implement ReAct patterns and chain-of-thought reasoning approaches).
Skill 2.1.3: Develop safeguarded AI workflows to ensure controlled FM behavior (for example, by using Step Functions to implement stopping conditions, Lambda functions to implement timeout mechanisms, IAM policies to enforce resource boundaries, circuit breakers to mitigate failures).
Skill 2.1.4: Create sophisticated model coordination systems to optimize performance across multiple capabilities (for example, by using specialized FMs to perform complex tasks, custom aggregation logic for model ensembles, model selection frameworks).
Skill 2.1.5: Develop collaborative AI systems to enhance FM capabilities with human expertise (for example, by using Step Functions to orchestrate review and approval processes, API Gateway to implement feedback collection mechanisms, human augmentation patterns).
Skill 2.1.6: Implement intelligent tool integrations to extend FM capabilities and to ensure reliable tool operations (for example, by using the Strands API to implement custom behaviors, standardized function definitions, Lambda functions to implement error handling and parameter validation).
Skill 2.1.7: Develop model extension frameworks to enhance FM capabilities (for example, by using Lambda functions to implement stateless MCP servers that provide lightweight tool access, Amazon ECS to implement MCP servers that provide complex tools, MCP client libraries to ensure consistent access patterns).
Task 2.2: Implement model deployment strategies.
Skill 2.2.1: Deploy FMs based on specific application needs and performance requirements (for example, by using Lambda functions for on-demand invocation, Amazon Bedrock provisioned throughput configurations, SageMaker AI endpoints to implement hybrid solutions).
Skill 2.2.2: Deploy FM solutions by addressing unique challenges of large language models (LLMs) that differ from traditional ML deployments (for example, by implementing container-based deployment patterns that are optimized for memory requirements, GPU utilization, and token processing capacity, by following specialized model loading strategies).
Skill 2.2.3: Develop optimized FM deployment approaches to balance performance and resource requirements for GenAI workloads (for example, by selecting appropriate models, by using smaller pre-trained models for specific tasks, by using API-based model cascading to perform routine queries).
Task 2.3: Design and implement enterprise integration architectures.
Skill 2.3.1: Create enterprise connectivity solutions to seamlessly incorporate FM capabilities into existing enterprise environments (for example, by using API-based integrations with legacy systems, event-driven architectures to implement loose coupling, data synchronization patterns).
Skill 2.3.2: Develop integrated AI capabilities to enhance existing applications with GenAI functionality (for example, by using API Gateway to implement microservice integrations, Lambda functions for webhook handlers, Amazon EventBridge to implement event-driven integrations).
Skill 2.3.3: Create secure access frameworks to ensure appropriate security controls (for example, by using identity federation between FM services and enterprise systems, role-based access control for model and data access, least privilege API access to FMs).
Skill 2.3.4: Develop cross-environment AI solutions to ensure data compliance across jurisdictions while enabling FM access (for example, by using AWS Outposts for on-premises data integration, AWS Wavelength to perform edge deployments, secure routing between cloud and on-premises resources).
Skill 2.3.5: Implement CI/CD pipelines and GenAI gateway architectures to implement secure and compliant consumption patterns in enterprise environments (for example, by using AWS CodePipeline, AWS CodeBuild, automated testing frameworks for continuous deployment and testing of GenAI components with security scans and rollback support, centralized abstraction layers, observability and control mechanisms).
Task 2.4: Implement FM API integrations.
Skill 2.4.1: Create flexible model interaction systems (for example, by using Amazon Bedrock APIs to manage synchronous requests from various compute environments, language-specific AWS SDKs and Amazon SQS for asynchronous processing, API Gateway to provide custom API clients with request validation).
Skill 2.4.2: Develop real-time AI interaction systems to provide immediate feedback from FM (for example, by using Amazon Bedrock streaming APIs for incremental response delivery, WebSockets or server-sent events to generate text in real time, API Gateway to implement chunked transfer encoding).
Skill 2.4.3: Create resilient FM systems to ensure reliable operations (for example, by using the AWS SDK for exponential backoff, API Gateway to manage rate limiting, fallback mechanisms for graceful degradation, AWS X-Ray to provide observability across service boundaries).
Skill 2.4.4: Develop intelligent model routing systems to optimize model selection (for example, by using application code to implement static routing configurations, Step Functions for dynamic content-based routing to specialized FMs, intelligent model routing based on metrics, API Gateway with request transformations for routing logic).
Task 2.5: Implement application integration patterns and development tools.
Skill 2.5.1: Create FM API interfaces to address the specific requirements of GenAI workloads (for example, by using API Gateway to handle streaming responses, token limit management, retry strategies to handle model timeouts).
Skill 2.5.2: Develop accessible AI interfaces to accelerate adoption and integration of FMs (for example, by using AWS Amplify to develop declarative UI components, OpenAPI specifications for API-first development approaches, Amazon Bedrock Prompt Flows for no-code workflow builders).
Skill 2.5.3: Create business system enhancements (for example, by using Lambda functions to implement customer relationship management [CRM] enhancements, Step Functions to orchestrate document processing systems, Amazon Q Business data sources to provide internal knowledge tools, Amazon Bedrock Data Automation to manage automated data processing workflows).
Skill 2.5.4: Enhance developer productivity to accelerate development workflows for GenAI applications (for example, by using Amazon Q Developer to generate and refactor code, code suggestions for API assistance, AI component testing, performance optimization).
Skill 2.5.5: Develop advanced GenAI applications to implement sophisticated AI capabilities (for example, by using Strands Agents and AWS Agent Squad for AWS native orchestration, Step Functions to orchestrate agent design patterns, Amazon Bedrock to manage prompt chaining patterns).
Skill 2.5.6: Improve troubleshooting efficiency for FM applications (for example, by using CloudWatch Logs Insights to analyze prompts and responses, X-Ray to trace FM API calls, Amazon Q Developer to implement GenAI-specific error pattern recognition).
