AWS AI Practitioner (AIF-C01)
Validate your foundational AI and machine learning knowledge on AWS
Exam Quick Facts
Exam Code
AIF-C01
Questions
65
Duration
120 minutes
Passing Score
700/1000 (~70%)
Cost
$100 USD
Valid For
3 years
Last Updated
2024
Difficulty
Beginner
AWS AI Practitioner Cheat Sheet (AIF-C01)
Model evaluation metrics, prompt engineering patterns, Amazon Bedrock, SageMaker AI, responsible AI guidelines, and every in-scope AWS service for the AIF-C01 exam
Certification Overview
The AWS Certified AI Practitioner (AIF-C01) is AWS's foundational certification for artificial intelligence and machine learning. Launched in 2024, it fills a gap in the certification landscape by providing a non-technical entry point for understanding AI/ML concepts within the AWS ecosystem.
This certification validates your understanding of AI/ML fundamentals, generative AI, foundation models, and responsible AI practices. It covers AWS services like Amazon Bedrock, SageMaker, Rekognition, Comprehend, and Lex, but at a conceptual level — you don't need to write code or train models.
With the explosive growth of generative AI and services like Amazon Bedrock, this certification has quickly become one of the most relevant credentials for anyone working alongside AI teams or making decisions about AI adoption in their organization.
Who Should Take This Exam?
The AI Practitioner is designed for business professionals, project managers, and early-career technologists who want to demonstrate AI/ML literacy. If your organization is adopting Amazon Bedrock for generative AI, or using SageMaker for model training, this cert proves you understand the concepts well enough to contribute to strategic discussions.
It's also a great starting point for developers and data analysts considering a move into ML engineering. Passing the AIF-C01 gives you the conceptual foundation before diving into the hands-on ML Engineer Associate certification.
Exam Domains
Fundamentals of AI and ML
Covers core AI/ML concepts: supervised vs. unsupervised learning, classification vs. regression, neural networks, training and inference, overfitting and underfitting, and evaluation metrics like accuracy and precision.
Fundamentals of Generative AI
Tests your understanding of foundation models, large language models, transformers, prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and the differences between generative AI approaches.
Applications of Foundation Models
The largest domain, covering how to select and use foundation models via Amazon Bedrock, build AI-powered applications, understand model capabilities and limitations, and implement common generative AI patterns.
Guidelines for Responsible AI
Covers responsible AI principles including fairness, bias detection and mitigation, transparency, explainability, human oversight, and ethical considerations when deploying AI systems.
Security, Compliance, and Governance for AI
Tests knowledge of data privacy in ML pipelines, model governance, access controls for AI services, compliance requirements, and how to securely handle training data and model outputs.
Ready to test your AIF-C01 knowledge?
Practice with our free AI Practitioner (AIF-C01) exam — 65 questions with detailed explanations. No signup required.
Start Free Practice ExamStudy Strategy
Begin with AWS's free AI Practitioner learning path, which covers AI fundamentals, generative AI concepts, and AWS AI services. Since this exam is conceptual, your study should focus on understanding "what" and "when" rather than "how to code."
Spend extra time on the largest domain — Applications of Foundation Models (28%). Make sure you understand Amazon Bedrock thoroughly: what foundation models are available, how to select the right model for a use case, what prompt engineering is, and how RAG works. Also learn the distinction between Bedrock (managed foundation models) and SageMaker (custom model training).
Practice with exam-style questions to get comfortable with the scenario-based format. Our free practice exam helps you identify which domains need more attention. Aim for 80%+ before booking the real exam. Most people need 2-3 weeks of study.
Key AWS Services to Know
- Amazon Bedrock — managed access to foundation models for generative AI
- Amazon SageMaker — build, train, and deploy ML models
- Amazon Rekognition — image and video analysis
- Amazon Comprehend — natural language processing (NLP)
- Amazon Lex — conversational AI (chatbots)
- Amazon Polly — text-to-speech
- Amazon Textract — extract text from documents
- Amazon Transcribe — speech-to-text
- Amazon Kendra — intelligent search
- Amazon Personalize — real-time personalization and recommendations
Video: AIF-C01 Exam Preparation
Career Impact
AI literacy is rapidly becoming a requirement across industries, not just in tech. The AI Practitioner certification positions you as someone who understands the AI landscape and can contribute to AI strategy discussions. For project managers and business analysts, it can differentiate you in a market where AI adoption is accelerating.
For technical professionals, it serves as a stepping stone to the ML Engineer Associate certification or a complement to other AWS certs. Organizations investing in generative AI increasingly look for team members who understand both the possibilities and limitations of AI — this certification proves that understanding.
Frequently Asked Questions
What AWS AI services should I know for the AIF-C01?
Focus on Amazon Bedrock, SageMaker, Rekognition, Comprehend, Polly, and Lex. Understanding when to use each service is more important than deep technical details.
Is the AI Practitioner harder than the Cloud Practitioner?
Similar difficulty, but you need familiarity with AI/ML concepts like supervised vs. unsupervised learning, neural networks, prompt engineering, and responsible AI principles. If you're new to both cloud and AI, start with the Cloud Practitioner first.
Do I need coding experience for the AI Practitioner exam?
No. The AIF-C01 is a conceptual exam. You need to understand AI/ML concepts and AWS AI services at a high level, but you won't be asked to write or debug code.
How does this relate to the old ML Specialty certification?
AWS replaced the ML Specialty (MLS-C01) with two new certs: the AI Practitioner (foundational, conceptual) and the ML Engineer Associate (hands-on, technical). The AI Practitioner is the entry point, while the ML Engineer is the hands-on successor.
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Ready to test your AIF-C01 knowledge?
Practice with our free AI Practitioner (AIF-C01) exam — 65 questions with detailed explanations. No signup required.
Start Free Practice Exam