Directory / Amazon Web Services

AWS Certified AI Practitioner

An entry-level exam on AI, machine learning and generative AI concepts and the AWS services around them. It tests vocabulary and judgement, not the ability to build.

What it signals

Practical assessment: Scenario-based multiple choice. Proctored knowledge exam; adds ordering and matching items but no code, case study or lab.
Identity assurance: Proctored with identity verification. Pearson VUE testing centre or online proctored
Depth: Foundational. Foundational level.
Experience expected: Under 6 months. Exam guide states up to 6 months of exposure.
Portability: Mixed. About half is general AI/GenAI concepts, the rest is AWS service identification (Bedrock, SageMaker AI, Amazon Q etc.).
Verifiability: Public badge or registry. Credly digital badge with verification, shareable by link.

For hiring managers

Proves vocabulary and conceptual literacy, not the ability to build: coding, data engineering, tuning and deployment are officially out of scope.
Can realistically be passed by study alone; target candidate has 'up to 6 months of exposure'.
Suitable for non-engineers (product, sales, management) as well as developers.
Holders certified before mid-2026 were tested on an older blueprint with far less agentic AI content.

Verifying it

Credly digital badge link shared by the candidate (badge has a verify function). No public registry.

What it covers

Published exam outline

Fundamentals of AI and ML 20%
Fundamentals of GenAI 24%
Applications of Foundation Models 28%
Guidelines for Responsible AI 14%
Security, Compliance, and Governance for AI Solutions 14%

By topic

GenAI apps: Core focus (over 35%). Domains 2 and 3 (GenAI fundamentals 24% + applications of foundation models 28%) total 52% of the blueprint, though at a conceptual level.
Agents: Touched on (under 10%). Agents, MCP and multi-agent patterns appear only as a few objectives (e.g. 2.1.6, 3.1.6) inside larger domains; estimated under 10%, no official sub-weight.
Classical ML: Some (10 to 20%). Domain 1 (20%) covers ML concepts and lifecycle descriptively; actual training and tuning are out of scope.
Operations: Touched on (under 10%). Only describes pipeline components and MLOps concepts within Domain 1; building pipelines is out of scope.
Data: Touched on (under 10%). Data types and data governance are touched on; data/feature engineering is explicitly out of scope.
Responsible AI: Substantial (20 to 35%). Domain 4 (14%) plus Domain 5 (14%) give 28% on responsible AI, security, compliance and governance.

Exam details

Who the provider says it is for. Individuals who want to validate foundational knowledge of AI/ML concepts and generative AI on AWS, across cloud, development, data, IT, AI/ML and business roles; people who use but do not necessarily build AI/ML solutions.

Format: multiple choice; multiple response; ordering; matching
Delivery: Pearson VUE testing centre or online proctored
Code or hands-on work: No. The exam guide explicitly lists 'Developing or coding AI/ML models or algorithms', data/feature engineering, hyperparameter tuning and building pipelines as OUT of scope. No labs.
Prerequisites: None
Recommended experience: Up to 6 months of exposure to AI/ML technologies on AWS. 'The target candidate uses but does not necessarily build AI/ML solutions on AWS.' Familiarity with core AWS services, shared responsibility model, IAM, pricing models.
Renewal: Pass the latest version of the AI Practitioner exam, OR pass AWS Certified Machine Learning Engineer - Associate, OR pass AWS Certified Generative AI Developer - Professional.

Preparing

Not confirmed

These details were not available from an official source at the time of research.

Exact Skill Builder prep-plan URL for AIF-C01 (Skill Builder pages are JavaScript-rendered and could not be read)
Whether Skill Builder prep resources are free or subscription-only
Share of blueprint devoted to agents (no official sub-domain weights)

Sources (5)