AWS Certified AI Practitioner: decisions and applications
Five lessons, 30 questions, and six cases on AI, generative models, RAG, evaluation, responsibility, and AWS security. AIF-C01 preparation.
Objectives and progression
Independent AIF-C01 preparation with five modules mapped to official domains in guide 1.1, published April 2026. Learn to choose between rules, ML, and foundation models, interpret metrics, compare cost and quality, use RAG, prompts, and agents, and assess security and responsibility. Practice fictional support situations: missed urgent cases, duplicate context, an old runbook, a student model, explainability, and a malicious document instruction. Includes option explanations, primary sources, progress, and an internal 21-decision assessment in 45 minutes. Initial coverage is selected; it does not reproduce the official 65-question exam or all its formats.
Audience: IT, project, support, and business professionals starting to assess AWS AI applications.
Prerequisites: Cloud, IAM, and application fundamentals; no AWS account or model programming required.
245 estimated study minutes
- Choose an approach suited to the desired outcome and interpret error costs.
- Relate models, tokens, context, and tools to value, cost, and boundaries.
- Distinguish retrieval, customization, and evaluation of complete applications.
- Assess limitations, group differences, and intended use before trusting an average.
- Control identity, data, tools, and evidence throughout the usage lifecycle.
Modules
- AI and ML: problem, data, and metrics
- Generative AI, context, and agents
- Foundation models: RAG, prompts, and evaluation
- Responsible AI and explainability
- AI security and governance
Continue learning
- AWS Certified Cloud Practitioner
- AI-900: Azure AI Fundamentals (historical)
- AWS Certified Machine Learning Engineer - Associate
- Application Production Support
References and version
AIF-C01
- AWS Certified AI Practitioner · 2026-10-01
- AWS Certified AI Practitioner exam guide · 2026-10-01
- AIF-C01 revisions · 2026-10-01
- AIF-C01 in-scope services · 2026-10-01
- AIF-C01 domain1: Fundamentals of AI and ML · 2026-10-01
- AIF-C01 domain2: Fundamentals of GenAI · 2026-10-01
- AIF-C01 domain3: Applications of Foundation Models · 2026-10-01
- AIF-C01 domain4: Guidelines for Responsible AI · 2026-10-01
- AIF-C01 domain5: Security, Compliance, and Governance for AI Solutions · 2026-10-01
- Amazon Bedrock overview · 2026-10-01
- Amazon Bedrock Knowledge Bases · 2026-10-01
- Amazon Bedrock Guardrails · 2026-10-01
- Amazon Bedrock evaluations · 2026-10-01
- Amazon Bedrock data protection · 2026-10-01
- Amazon Bedrock Prompt management · 2026-10-01
- Amazon Bedrock model customization · 2026-10-01
- Amazon Bedrock IAM · 2026-10-01
- Amazon Bedrock AgentCore overview · 2026-10-01
- Amazon SageMaker Model Cards · 2026-10-01
- SageMaker model explainability · 2026-10-01
- SageMaker Clarify availability change · 2026-10-01
What you will explore
0 / 5AI and ML: problem, data, and metrics
Choose an approach suited to the desired outcome and interpret error costs.
Generative AI, context, and agents
Relate models, tokens, context, and tools to value, cost, and boundaries.
Foundation models: RAG, prompts, and evaluation
Distinguish retrieval, customization, and evaluation of complete applications.
Responsible AI and explainability
Assess limitations, group differences, and intended use before trusting an average.
AI security and governance
Control identity, data, tools, and evidence throughout the usage lifecycle.