AWS Certified AI Practitioner (AIF-C01)
Everything you need to pass the AWS Certified AI Practitioner exam. Comprehensive coverage of all 4 domains, 30+ practice questions with detailed explanations, study plans, and exam-day strategies — all free. The perfect entry-level AI certification for beginners.
Your Study Path
Follow these lessons in order for complete exam preparation, or jump to any domain you need to review.
1. Exam Overview
Exam format (85 questions, 120 min), domain weights, cost ($150), study plans, registration process, and what to expect on exam day.
2. AI/ML Fundamentals (20%)
AI vs ML vs deep learning, supervised and unsupervised learning, the ML model lifecycle, and key terminology. Includes practice questions.
3. Generative AI Fundamentals (24%)
Foundation models, LLMs, prompt engineering, RAG, Amazon Bedrock, and real-world generative AI applications. Practice questions included.
4. AWS AI/ML Services (36%)
SageMaker, Bedrock, Rekognition, Comprehend, Textract, Polly, Lex, and more. The highest-weighted domain with practice questions.
5. Responsible AI (20%)
Bias detection, fairness, transparency, explainability, security, governance, and AWS tools for responsible AI. Practice questions included.
6. Practice Exam
30 exam-style questions covering all 4 domains with detailed explanations for every answer choice. Simulate real exam conditions.
7. Exam Tips
Last-minute review sheet, exam day strategy, time management, frequently asked questions, and additional study resources.
What You'll Learn
By the end of this course, you will be ready to:
Pass the AIF-C01 Exam
Achieve the 700/1000 score needed to earn your AWS Certified AI Practitioner certification on your first attempt.
Understand AI/ML on AWS
Know when and how to use SageMaker, Bedrock, Rekognition, Comprehend, Textract, Polly, Lex, and other AWS AI services.
Master Generative AI Concepts
Understand foundation models, LLMs, prompt engineering, RAG, and how Amazon Bedrock enables generative AI workloads.
Apply Responsible AI
Identify bias, ensure fairness, implement governance, and use AWS tools for building trustworthy and transparent AI systems.
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