Content Domain 2: Fundamentals of GenAI
Domain 2 covers the fundamentals of GenAI and represents 24% of the scored content on the exam.
Tasks
Task Statement 2.1: Explain the basic concepts of generative AI (GenAI).
Objectives:
Define foundational GenAI concepts (for example, tokens, chunking, embeddings, vectors, prompt engineering, transformer-based large language models [LLMs], foundation models [FMs], multi-modal models, diffusion models).
Identify potential use cases for GenAI models (for example, image, video, and audio generation; summarization; AI assistants; translation; code generation; customer service agents; search; recommendation engines).
Describe the FM lifecycle (for example, data selection, model selection, pre-training, fine-tuning, evaluation, deployment, feedback).
Describe the token-based pricing model and its effect on cost and performance for inference.
Describe the role of context engineering in FM applications.
Define foundational agentic AI concepts (for example, multi-agent system patterns for complex AI applications, Model Context Protocol [MCP] and its role in connecting agents to external systems, multi-agent communication patterns, memory management, tool usage, and workflow orchestration).
Task Statement 2.2: Understand the capabilities and limitations of GenAI for solving business problems.
Objectives:
Describe the advantages of GenAI (for example, adaptability, responsiveness, conversational capabilities, ability to generate content).
Identify disadvantages of GenAI solutions (for example, hallucinations, interpretability, inaccuracy, nondeterminism).
Identify factors to consider when selecting GenAI models (for example, model types, performance requirements, capabilities, constraints, compliance, cost, latency, model complexity).
Determine business value and metrics for GenAI applications (for example, cross-domain performance, ROI, efficiency, conversion rate, average revenue per user, accuracy, customer lifetime value).
Task Statement 2.3: Describe AWS infrastructure and technologies for building GenAI applications.
Objectives:
Identify AWS services and features to develop GenAI applications (for example, Amazon Bedrock, Amazon SageMaker AI, SageMaker JumpStart, Amazon Quick, Kiro, Strands Agents, Amazon Bedrock AgentCore).
Describe the advantages of using AWS GenAI services to build applications (for example, accessibility, lower barrier to entry, efficiency, cost-effectiveness, speed to market, ability to meet business objectives).
Describe the benefits of AWS infrastructure for GenAI applications (for example, security, compliance, responsibility, safety).
Describe cost tradeoffs of AWS GenAI services (for example, responsiveness, availability, redundancy, performance, regional coverage, token-based pricing, provision throughput, custom models).
