2026 Exam Guide
AWS Certified AI Practitioner Study Guide
Current exam coverage, candidate guidance, important topics, and practical preparation advice for the AIF-C01 exam.
What Is AWS AI Practitioner?
AWS Certified AI Practitioner is a foundational certification for people who need to understand artificial intelligence, machine learning, generative AI, and AWS AI services without becoming specialist ML engineers. The AIF-C01 exam validates AI vocabulary, use-case selection, responsible AI concepts, foundation model basics, security, and AWS service positioning.
In 2026, candidates should understand how organizations evaluate AI use cases, prepare data, choose managed AI services, use Amazon Bedrock and generative AI capabilities, apply guardrails, protect data, and measure business value. The exam is conceptual but scenario-based, so candidates must connect AI requirements to appropriate AWS capabilities.
Who Should Take This Exam?
This certification is useful for cloud beginners, business analysts, product managers, sales engineers, project managers, security stakeholders, and technical professionals who work around AI projects.
It is also a helpful starting point before deeper AWS machine learning or data certifications. Candidates do not need to train models from scratch, but they should understand AI terminology, risk, and service selection.
Exam Domains
Fundamentals of AI and ML
CoreAI, ML, deep learning, generative AI, model types, evaluation, and use cases.
Fundamentals of Generative AI
CoreFoundation models, prompts, embeddings, RAG, agents, and model selection.
Applications of Foundation Models
CoreAmazon Bedrock, managed AI services, customization, grounding, and business use cases.
Responsible AI
CoreFairness, explainability, privacy, safety, governance, and risk controls.
Security, Compliance, and Governance
CoreData protection, access control, monitoring, compliance, and AI governance.
Common Topics Covered
- Amazon Bedrock
- Foundation models
- RAG
- Embeddings
- Prompt engineering
- Amazon Q
- SageMaker basics
- Responsible AI
- Data privacy
- AI governance
Study Tips
Focus on service and use-case matching. Know when a managed AI service, Amazon Bedrock, Amazon Q, or SageMaker-style workflow fits a requirement.
Review responsible AI and security carefully. Foundational AI questions often ask how to reduce risk, protect data, evaluate outputs, or choose a safer implementation pattern.
Practice Questions Overview
Certoga's AWS AI Practitioner questions help candidates practice AI service selection, responsible AI reasoning, and generative AI fundamentals through original scenarios.