AWS 100

AWS AI Practitioner — AIF-C01 Exam Preparation

This instructor-led course covers foundational AI, machine learning, and generative AI concepts in the context of AWS services.

Description

This instructor-led course covers foundational AI, machine learning, and generative AI concepts in the context of AWS services. Participants learn what AI/ML is, how AWS services like Bedrock, SageMaker, and Comprehend fit into business workflows, and how to evaluate AI solutions responsibly. The course covers all five AIF-C01 exam domains: Fundamentals of AI and ML (20%), Fundamentals of Generative AI (24%), Applications of Foundation Models (28%), Guidelines for Responsible AI (14%), and Security, Compliance, and Governance for AI Solutions (14%). No prior AI or machine learning experience is required. Participants leave the course able to explain AI/ML concepts, identify appropriate AWS AI services for business use cases, describe responsible AI practices, and navigate AI security and governance on AWS. NOTE: This course targets the AIF-C01 exam blueprint released by AWS. The AIF-C01 exam consists of 65 questions (50 scored + 15 unscored) with a passing score of 700 on a 100–1000 scale.

Who This Course Is For

  • Business analysts, product managers, and technical leads evaluating AI/ML capabilities on AWS
  • Anyone preparing for the AWS Certified AI Practitioner (AIF-C01) exam
  • Technical and non-technical professionals who need to understand AI/ML concepts without building models
  • Sales engineers and solutions architects who need foundational AI/ML literacy for client conversations
  • Decision-makers evaluating responsible AI practices and governance requirements

What You Will Learn

  • Explain fundamental AI and ML concepts, identify practical use cases, and describe the ML development lifecycle using AWS services.
  • Explain generative AI concepts, evaluate capabilities and limitations of GenAI, and identify AWS infrastructure and services for generative AI workloads.
  • Describe design considerations for foundation model applications, including model selection, RAG, and prompt engineering techniques.
  • Describe training, fine-tuning, and evaluation methods for foundation models, including performance metrics and business alignment.
  • Explain responsible AI principles including bias, fairness, transparency, and explainability, and identify AWS tools for responsible AI development.
  • Explain security methods, compliance requirements, and governance frameworks for AI solutions on AWS.

Course Outline

  • Foundations of AI and Machine Learning
  • Generative AI Concepts and AWS Services
  • Designing Foundation Model Applications
  • Training, Fine-Tuning, and Evaluating Foundation Models
  • Responsible AI Practices
  • Security, Compliance, and Governance for AI

This is a high-level overview. For the complete syllabus with detailed topics and lab descriptions, request the full syllabus.

Prerequisites

  • Basic familiarity with AWS services (EC2, S3, Lambda, IAM, shared responsibility model)
  • Comfort navigating the AWS Management Console
  • No prior AI, machine learning, or data science experience required
  • An AWS account with console access for hands-on exploration activities
  • A modern web browser and reliable internet connection

Delivery Options

  • Live, instructor-led

Bring This Course to Your Team

This course is delivered as private, instructor-led training for teams and organizations. Contact us for a quote, scheduling, and group options.

Contact Us for a Quote Request Full Syllabus