AWS Certified AI Practitioner

AIF-C01 · ● Active · Entry · AWS

Exam facts

Vendor source — AWS Certification ↗

Official exam guide — AWS Certified AI Practitioner Exam Guide PDF ↗

Exam objectives — AWS Certified AI Practitioner Documentation ↗

About

The AWS Certified AI Practitioner validates foundational knowledge of artificial intelligence (AI), machine learning (ML), and generative AI (GenAI) concepts, with focus on practical business applications of AI on AWS. Launched in August 2024 (beta) and reaching general availability in October 2024, AIF-C01 targets business users, product managers, sales engineers, and IT professionals seeking to understand AI/ML without requiring deep technical expertise. It is the entry point to AWS's AI/ML certification ladder, positioned below the MLA-C01 (Machine Learning Engineer – Associate) and intended to democratize AI literacy across non-specialist IT and business roles.

Domain context — Data / AI

The Data / AI domain encompasses data engineering, analytics, machine learning, and artificial intelligence roles. AIF-C01 sits at the entry level, introducing foundational AI/ML concepts and AWS AI services. Career progression typically flows: Data Analyst → Data Engineer → ML Engineer or AI Engineer, with AIF-C01 serving as an early validation for career changers and business professionals pivoting toward AI-focused roles.

Read full deep dive — Data / AI Domain →

Topics covered

The exam validates mastery of five content domains:

• Fundamentals of AI and ML (20%) — Definitions of AI, ML, deep learning; supervised / unsupervised learning; use case distinctions; basic model selection
• Fundamentals of Generative AI (24%) — Foundation models, large language models (LLMs), diffusion models, multimodal models, prompt engineering, in-context learning
• Applications of Foundation Models (28%) — Foundation model selection and deployment; retrieval-augmented generation (RAG); fine-tuning strategies; AWS Bedrock and SageMaker integration
• Guidelines for Responsible AI (14%) — AI ethics, bias detection, hallucination mitigation, fairness frameworks, responsible AI principles
• Security, Compliance, and Governance for AI Solutions (14%) — Data privacy, model governance, Amazon SageMaker Model Cards, compliance frameworks, MLOps practices

Source: Official exam guide ↗

Common skills at Data / AI · Entry

Shared content for the Data / AI domain at Entry level — not specific to this cert.

• AI vs ML vs DL conceptual distinctions
• Supervised and unsupervised learning paradigms
• Training, validation, and test dataset concepts
• Feature engineering and data quality fundamentals
• Classification, regression, and clustering task types
• Introduction to neural networks and embeddings

Recommended courses at Data / AI · Entry

Practice exams

Books

Note: The Sybex study guide is written by AWS internal specialists (Senior Generative AI Specialist Solutions Architect, Sr. Generative AI Specialist Architect, and Principal Data Scientist at AWS). It includes online access to practice tests, chapter review questions, and flashcards.

Typical job titles at Data / AI · Entry

AI Practitioner · Junior AI Engineer · AI Sales Engineer · Pre-Sales AI Solutions Engineer · Business Analyst (AI/ML) · Product Manager (AI/ML focus) · Data AI Consultant

(Job titles drawn from current job-board postings and AWS certification marketing targeting business analysts, IT support specialists, marketing professionals, product managers, line-of-business managers, and sales professionals.)

Salary

Salary note: Entry-level AI roles in the US average $126,000–$142,000 (AI Specialist base). Companies offer salary premiums of 40–47% for IT professionals with AI skills. FAANG and well-funded startups typically offer top-of-range or above. Regional variations: San Francisco / New York command +20–40% premium.

Skills validated

Cert-specific — what this exam actually tests, distinct from the shared "Common skills" above.

• AWS Bedrock (foundation model access and deployment)
• Amazon SageMaker (basic ML workflows, notebooks, training, hosting)
• Amazon Q Developer / Q Business (AWS AI coding and business assistant)
• Amazon Comprehend (natural language processing and sentiment analysis)
• Amazon Translate (machine translation)
• Amazon Polly (text-to-speech)
• Amazon Transcribe (speech-to-text)
• Amazon Rekognition (computer vision and image analysis)
• Amazon Lex (chatbot frameworks)
• Amazon Personalize (recommendation systems)
• Prompt engineering (zero-shot, few-shot, chain-of-thought)
• Retrieval-augmented generation (RAG) concept
• Vector embeddings and semantic search
• Model evaluation and selection frameworks
• Responsible AI tools and governance with Amazon SageMaker Model Cards
• Foundation model lifecycle (data selection, pre-training, fine-tuning, deployment, feedback)

Related certifications

• Stacks with: AWS Certified Machine Learning Engineer – Associate (MLA-C01) ↗ · AWS Certified Data Engineer – Associate (DEA-C01) ↗
• Entry point before: MLA-C01 (technical ML implementation) · Emerging AWS Generative AI Developer – Professional
• Equivalents at this level: Microsoft Azure AI Fundamentals (AI-900) ↗ · Google Cloud Generative AI Leader (GCGAL) ↗
• Vendor overview: AWS Vendor Overview ↗

Sources

• AWS Certified AI Practitioner (AIF-C01) Certification Page
• AWS Certified AI Practitioner Exam Guide PDF
• AWS Certified AI Practitioner Official Documentation
• AWS Certification Exam Guides (Master List)
• AWS Skill Builder – Official Practice Exam
• Tutorials Dojo – AWS AIF-C01 Practice Exams
• Udemy – Ultimate AWS Certified AI Practitioner AIF-C01 (Stéphane Maarek)
• Sybex – AWS Certified AI Practitioner Study Guide
• Glassdoor – Artificial Intelligence Specialist Salary
• Coursera – Artificial Intelligence Salary Guide
• Tech Jacks Solutions – AWS AI Practitioner Certification Career Blueprint
• AWS Training Blog – Mapping Your AI/ML Career Journey

Last verified: 2026-05-01 Parent ecosystem: AWS Ecosystem Parent domain: Data / AI Domain Vendor overview: AWS Vendor Overview