AAIA study guide
AAIA · Work through your exam, one topic at a time.
The AWS Certified AI Practitioner (AIF-C01) is a foundational-level credential for people who work with AI and machine learning on AWS but are not necessarily building models themselves. It suits sales, product, project, operations, and early-career technical roles that need a working vocabulary for foundation models, generative AI, and the AWS services that support them. The exam is organized into five domains: fundamentals of AI and ML, fundamentals of generative AI, applications of foundation models, guidelines for responsible AI, and security, compliance, and governance for AI solutions. Generative AI and foundation-model application carry the most weight, so expect the bulk of the questions to test how models are chosen, prompted, evaluated, and integrated rather than deep model mathematics. The AAIA study guide has 5 sections and about 1,503 words.
01Fundamentals of AI and ML
You must separate AI, machine learning, and deep learning; choose a learning paradigm from a scenario; and read evaluation metrics correctly. Service selection (custom SageMaker builds vs.
294 words, about 1 min02Fundamentals of Generative AI
How foundation models are built and how their controls affect cost, latency, and quality - plus when Bedrock, SageMaker JumpStart, or a prebuilt generative feature is the right vehicle. Transformers use self-attention to weigh relationships among all tokens at once.
317 words, about 2 min03Applications of Foundation Models
Choosing and operating the technique that makes a foundation model useful in production - prompt engineering, RAG, agents, or customization - and proving it works through evaluation. Prompt engineering is cheapest and fastest: zero-shot, few-shot (examples embedded in the prompt), chain-of-thought (request explicit steps), and structured output templates.
313 words, about 2 min04Guidelines for Responsible AI
The responsible-AI dimensions - fairness, explainability, privacy, safety, transparency, and human oversight - and which concrete control implements each one. Bias enters through data, labeling, and feature choice.
285 words, about 1 min05Security, Compliance, and Governance for AI Solutions
Securing AI workloads and governing them: identity, encryption, network isolation, logging, compliance evidence, and the threats specific to generative AI. Identity: IAM roles with least privilege, scoped per model and per action; separate roles for training, inference, and data access.
294 words, about 1 min