AWS Certified AI Practitioner (AIF-C01)

The AWS Certified AI Practitioner (AIF-C01) exam assesses foundational understanding of artificial intelligence concepts and their practical application using AWS services, with emphasis on business-oriented decision making rather than hands-on model development. Candidates are expected to recognize and explain core AI and machine learning terminology, distinguish generative AI capabilities, and map technology options to business problems. The exam evaluates the ability to choose appropriate AWS services and solution patterns—such as when to leverage managed AI offerings, foundation models, or inference endpoints—while accounting for operational considerations like cost, scalability, and performance. It also measures understanding of the AWS shared responsibility model, identity and access management basics, and service pricing considerations that affect solution selection. The intended candidate typically has up to six months of exposure to AI/ML on AWS and uses AI-enabled features but is not required to build, code, or optimize models; coding, data engineering, hyperparameter tuning, pipeline construction, detailed statistical analysis, and full governance implementation are explicitly out of scope. Assessment content is organized into five weighted 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. Across these domains, the exam emphasizes responsible and ethical use, privacy and compliance considerations, reliability and quality assurance, and high-level governance principles appropriate for practitioners advising on or selecting AI solutions. Question formats test scenario-based reasoning and knowledge recognition through multiple-choice, multiple-response, ordering, and matching-style items. The exam is intended to confirm that a candidate can translate business needs into appropriate AI approaches on AWS, articulate trade-offs and risks, and recommend responsible deployment and monitoring strategies without requiring deep engineering or research-level expertise.