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 GenAI.
Objectives:
Define foundational GenAI concepts (for example, tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models [FMs], multimodal 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 foundation model lifecycle (for example, data selection, model selection, pre-training, fine-tuning, evaluation, deployment, feedback).
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, simplicity).
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).
Determine business value and metrics for GenAI applications (for example, cross-domain performance, 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 SageMaker JumpStart, Amazon Bedrock PartyRock, Amazon Q, Amazon Bedrock Data Automation).
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).
