2026.06.10 2026.09.22

AIF-C01 Domain 2 Complete Guide: Fundamentals of Generative AI (24%)

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Domain 2, Fundamentals of Generative AI, carries 24% of the AIF-C01 exam — the second-largest weight. Together with Domain 3 it forms the generative-AI core that distinguishes this certification from earlier AWS foundational exams. This guide covers the vocabulary, the model types, the foundation-model lifecycle, the honest limits of generative AI, and the AWS stack you are expected to recognize.

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The shape of Domain 2 — three task statements
Core concepts — tokens, embeddings, chunking
Types of generative models — LLM, foundation, multimodal, diffusion
The foundation-model lifecycle — the official seven stages
Strengths and limits — hallucination, non-determinism, interpretability
Model-selection factors and business-value metrics
The AWS generative-AI stack — Bedrock, Amazon Q, PartyRock, JumpStart
Why build on AWS — pricing model and security
Domain 2 on the workbench — applying it to making
Summary — the scoring strategy for Domain 2
References

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The shape of Domain 2 — three task statements

The three task statements are: explain basic concepts of generative AI; understand the capabilities and limitations of generative AI for solving business problems; and describe AWS infrastructure and technologies for building generative-AI applications. The thread running through all three is judgment about fit and trade-offs, not implementation detail.

A current study guide is the fastest way to turn scattered reading into exam-ready coverage.

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Core concepts — tokens, embeddings, chunking

A token is the unit a model reads and generates — roughly a word fragment; pricing and context limits are measured in tokens. An embedding is a numeric vector that captures meaning, so that similar concepts sit close together in vector space. Chunking splits a long document into passages before embedding, which is the first step of retrieval. Learn the chain document to chunking to embedding to vector — ordering questions test exactly this flow.

Types of generative models — LLM, foundation, multimodal, diffusion

Large language models (LLMs) generate and understand text.
Foundation models are large models pre-trained on broad data and adaptable to many tasks; LLMs are a subset.
Multimodal models handle more than one modality — text plus images or audio.
Diffusion models generate images by iteratively denoising, the basis of most image generators.

Match the model type to the use case — a matching-question favorite. Image generation points to diffusion; a document chatbot points to an LLM; a system that reads a photo and answers questions points to multimodal.

The foundation-model lifecycle — the official seven stages

The exam guide frames the foundation-model lifecycle in seven stages: data selection, pre-training, optimization (such as fine-tuning), evaluation, deployment, feedback, and continuous improvement. Do not confuse this seven-stage FM lifecycle with the nine-stage ML lifecycle from Domain 1 — questions deliberately mix them.

Strengths and limits — hallucination, non-determinism, interpretability

Generative AI excels at drafting, summarizing, translating, coding, and ideation. Its limits are testable and worth memorizing as four points: hallucination (confident but false output), inaccuracy on facts, non-determinism (the same prompt can yield different answers), and limited interpretability (you cannot fully explain why a given output was produced). These limits drive design choices such as RAG and human review in later domains.

Model-selection factors and business-value metrics

Choosing a model balances capability, latency, cost, context-window size, modality, and customizability. On the business side, the exam expects value framing: time saved, conversion lift, cost avoided, and customer satisfaction. A larger, pricier model is not automatically better — the right answer is usually the smallest model that meets the requirement.

The AWS generative-AI stack — Bedrock, Amazon Q, PartyRock, JumpStart

Service	Role
Amazon Bedrock	Managed access to foundation models from multiple providers via one API
Amazon Q Developer	Coding assistant — free tier plus Pro at $19/user/month
Amazon Q Business	Enterprise assistant over your documents — Lite $3, Pro $20 per user/month
PartyRock	No-code playground for building generative-AI apps
SageMaker JumpStart	Hub of pre-trained models and solution templates

Why build on AWS — pricing model and security

The exam frames AWS advantages as consumption-based pricing (pay per token or per hour, no upfront model training), a wide choice of models through Bedrock, and built-in security: your data is not used to train the base models, and it stays within your AWS environment with IAM and encryption. These points answer “why AWS” questions directly.

Pricing tables make sense once you understand how foundation-model applications actually consume tokens.

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Domain 2 on the workbench — applying it to making

For a solo maker, Domain 2 is the most immediately useful section. Choosing a smaller model for a product-description generator, using embeddings to search your own build logs, or recognizing when non-determinism will frustrate users — these are everyday decisions. The exam vocabulary simply gives you a precise way to reason about them.

Summary — the scoring strategy for Domain 2

Domain 2 (24%) rewards clean vocabulary: tokens, embeddings, and chunking; the four model types; the seven-stage FM lifecycle; the four limits; and the AWS stack. Lock these in and you carry momentum into the heaviest domain, Domain 3.