AIF-C01 Domain 2 - Fundamentals of Generative AI
Domain 2 is 24% of the scored AWS Certified AI Practitioner (AIF-C01) exam. It covers foundation-model vocabulary, lifecycle and economics, model selection, agentic AI, and AWS generative-AI options.
Core concepts
- Token: a model’s unit of text input/output; it can be a character, subword, word, or punctuation sequence.
- Context window: the maximum tokens a model can consider for a request, including instructions, conversation, retrieved context, tool results, and often the generated output.
- Chunking: splitting source content into retrievable segments. Chunks that are too small lose context; chunks that are too large dilute relevance and consume the context window.
- Embedding: a numeric vector representing semantic properties of data.
- Vector: an ordered list of numbers. Similar vectors are found with distance/similarity measures for semantic search.
- Transformer: a neural architecture based on attention that processes relationships among tokens and underpins most modern LLMs.
- Multimodal model: accepts or generates more than one modality, such as text, image, audio, or video.
- Diffusion model: learns to reverse a noise-adding process and is commonly used for image/audio/video generation.
- Prompt engineering: designing the instructions and examples passed to a model.
- Context engineering: managing the complete information environment around the model - system instructions, conversation, retrieval, memory, tool descriptions/results, and token budget.
Foundation-model lifecycle
- Select and govern broad pretraining data.
- Select an architecture and pretrain the base model.
- Adapt with instruction tuning, domain fine-tuning, continued pretraining, preference alignment, or distillation.
- Evaluate quality, safety, robustness, bias, cost, and business fit.
- Deploy for inference with the required latency, availability, and capacity.
- Collect feedback, monitor, and improve prompts, retrieval, guardrails, or model versions.
Most exam scenarios start from an existing FM. Training a foundation model from scratch is the most expensive and data-intensive option.
Token economics
- Usage-based inference commonly charges separately for input and output tokens; long prompts, large retrieved contexts, and verbose answers raise cost and latency.
- A larger context window is capability, not free memory. Include only relevant information.
- Prompt caching can reduce repeated processing cost/latency for stable prompt prefixes when supported.
- On-demand inference favors variable usage; provisioned throughput favors predictable capacity/latency but introduces commitment and utilization risk.
- Smaller models are often cheaper/faster; larger or reasoning models may improve difficult-task quality.
Agentic AI
An AI agent uses a model to interpret a goal, plan, call tools, observe results, use memory, and continue until it returns an answer or completes an action.
- Tool: a callable API, function, database query, browser, or code interpreter.
- Memory: short- or long-term state that preserves relevant history beyond a single model request.
- Workflow orchestration: controlling sequence, branching, retries, approvals, and failure handling.
- Multi-agent system: specialized agents coordinate through supervisor, swarm, graph, or workflow patterns. It can improve separation of responsibilities but raises cost, latency, security, and observability complexity.
- Model Context Protocol (MCP): an open protocol for exposing tools and contextual resources to compatible AI applications. MCP provides interoperability; authentication, authorization, and safe tool use still need explicit controls.
Use cases
Generative AI is suited to summarization, assistants, semantic search, content/image/audio/video generation, translation, code generation, recommendation explanations, document processing, and customer-service agents. It is less appropriate when output must be deterministic, perfectly factual, trivially produced by rules, or cannot be safely reviewed/validated.
Benefits and limitations
| Benefits | Limitations and risks |
|---|---|
| adaptable across tasks | plausible but false hallucinations |
| natural conversational interface | nondeterministic output |
| rapid prototyping and time to market | limited explainability |
| creates and transforms unstructured content | bias, toxicity, privacy/IP risk |
| lowers the entry barrier to broad AI tasks | token cost, latency, and regional/model constraints |
Grounding, validation, Guardrails, human review, and smaller task-specific models reduce different risks; no single technique guarantees accuracy.
Model selection criteria
- modality and task capability;
- quality on representative data, not marketing benchmarks alone;
- context/input/output limits and supported languages;
- latency, throughput, availability, and regional coverage;
- token cost, customization cost, and operational complexity;
- safety, licensing, data-use terms, explainability, and regulatory requirements;
- model size/complexity and whether a traditional ML model is more appropriate.
AWS choices
- Amazon Bedrock - managed API access to multiple FMs plus Knowledge Bases, Guardrails, evaluations, prompt management, and other GenAI building blocks.
- Amazon SageMaker AI - deeper control over custom/open models, training, fine-tuning, hosting, and the ML lifecycle.
- Amazon SageMaker JumpStart - pretrained models/algorithms deployable into SageMaker AI infrastructure.
- Amazon Nova - Amazon’s multimodal FM family, accessed through Bedrock and customizable through supported AWS workflows.
- Amazon Quick - AI workspace for research, analysis, content, applications, and automation.
- Amazon Q - developer/AWS assistance; Q Business is closed to new customers after July 31, 2026, with Quick as the current enterprise-work successor.
- Kiro - agentic coding environment.
- Strands Agents - open-source SDK for tool-using and multi-agent applications.
- Amazon Bedrock AgentCore - secure runtime and operational primitives for agents built with various models/frameworks.
Common traps
- Embeddings represent meaning for retrieval; they do not contain the original document in human-readable form.
- Chunking, embedding, retrieval, and generation are separate steps.
- More context can increase cost and introduce irrelevant/conflicting evidence.
- Agents add action-taking and orchestration around models; they are not simply chatbots.
- Accessibility and speed do not remove governance, data-quality, or human-accountability requirements.