Exam Prep Standard Course: AWS Certified AI Practitioner (AIF-C01)

In this fundamental-level course from Amazon Web Services (AWS), you learn how to assess your preparedness for the AWS Certified AI Practitioner (AIF-C01) exam. The AWS Certified AI Practitioner (AIF-C01) exam validates in-demand knowledge of AI, machine learning (ML), and generative AI concepts and use cases.

Prepare for the exam by exploring the exam’s topic areas and how they align to developing on AWS and to specific areas of study. Gauge your understanding of topics and concepts from each task statement grouped by domain. Reinforce your knowledge and identify learning gaps with explanations of exam-style questions. Follow the instructor as they review exam-style questions. Learn test-taking strategies to identify incorrect responses.

The standard exam prep course is one step in the 4-step plan that you can use to prepare for your exam with confidence. To access resources for the comprehensive 4-step plan, enroll in the Enhanced Exam Prep Plan: AWS Certified AI Practitioner (AIF-C01), which includes videos, hands-on labs, additional exam-style questions, a pretest, and flashcards. If you are already logged into AWS Skill Builder, use this link version to access the plan.

The Standard Exam Prep Plan: AWS Certified AI Practitioner (AIF-C01) includes free resources for Steps 1–3 of the 4-step plan. If you are already logged into AWS Skill Builder, use this link version to access the plan.

 

AWS updates and occasionally retires services and features as part of ongoing development. While Exam Prep content is regularly updated, there are brief periods when our courses may not reflect the current state of AWS services. We recommend checking the latest AWS documentation and announcements for the most accurate and up-to-date information about the current availability of services and features.

In August 2024, AWS announced that we are removing access to a number of services or features for new customers, including several included in this course. These include: AWS Cloud9 and Amazon Forecast. We will remove references in the next course update.

Course level: Fundamental
Duration: 8 hours 

Activities

This course includes the following:

• Videos by expert instructors who deliver presentations and reviews exam-style questions.
• Official Practice Questions (Question Set) written in same style as AWS Certification exams. All questions include detailed feedback and recommended resources to help you prepare for the exam.

Course objectives

In this course, you will do the following:
1.    Understand the knowledge tested by the AWS Certified AI Practitioner (AIF-C01) exam.
2.    Evaluate your gaps in knowledge of the exam topics.

Intended audience

This course is intended for individuals who meet the following requirements:
1.    Have up to 6 months of exposure to AI and ML technologies on AWS.
2.    Are familiar with, but do not necessarily build, solutions using AI and ML technologies on AWS.
3.    Are preparing for the AWS Certified AI Practitioner (AIF-C01) exam.

Prerequisites

These are the prerequisites for the AWS Certified AI Practitioner (AIF-C01) exam.

Recommended AWS knowledge
Learners should have the following:

• Familiarity with the core AWS services (for example, Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker) and AWS core services use cases
• Familiarity with the AWS shared responsibility model for security and compliance in the AWS Cloud
• Familiarity with AWS Identity and Access Management (IAM) for securing and controlling access to AWS resources
• Familiarity with the AWS global infrastructure, including the concepts of AWS Regions, Availability Zones, and edge locations
• Familiarity with AWS service pricing models

Recommended courses

Although we don't require that you take any specific training before you take an exam, we do recommend that learners take the following courses (or similar courses) before taking this course.

• Fundamentals of Machine Learning and Artificial Intelligence (1 hour)
• Exploring Artificial Intelligence Use Cases and Applications (1 hour)
• Responsible Artificial Intelligence Practices (1 hour)
• Developing Machine Learning Solutions (1 hour)
• Developing Generative Artificial Intelligence Solutions (1 hour)
• Essentials of Prompt Engineering (1 hour)
• Optimizing Foundation Models (1 hour)
• Security, Compliance, and Governance for AI Solutions (1 hour)
• Generative AI for Executives (0.25 hour)
• Amazon Q Business Getting Started (0.75 hour)
• Amazon Bedrock Getting Started (1 hour)
• Getting Started with Amazon Comprehend: Custom Classification (1.25 hours)
• Build a Question-Answering Bot Using Generative AI (1.5 hours)

To enroll in these courses, we recommend enrolling in the Standard Exam Prep Plan: AWS Certified AI Practitioner (AIF-C01).

Course outline

Module 1: Get to Know the Exam with Exam-Style Questions

• Introduction to AWS Certified AI Practitioner
• Exam Guide: AWS Certified AI Practitioner
• Introduction to Exam-Style Questions
• Official Practice Question Set: AWS Certified AI Practitioner (AIF-C01)

Module 2: Refresh Your AWS Knowledge and Skills

• AWS Training Recommendations 
• Whitepapers and FAQs

Module 3: Review and Practice

Domain 1:  Fundamentals of AI and ML

• Introduction
• Task Statement 1.1: Explain basic AI concepts and terminologies
• Task Statement 1.2: Identify practical use cases for AI
• Task Statement 1.3: Describe the ML development lifecycle
• Walkthrough Questions
• Additional Resources

Domain 2: Fundamentals of Generative AI

• Introduction
• Task Statement 2.1: Explain the basic concepts of generative AI
• Task Statement 2.2: Understand the capabilities and limitations of generative AI for solving business problems
• Task Statement 2.3: Describe AWS infrastructure and technologies for building generative AI applications
• Walkthrough Questions
• Additional Resources

Domain 3: Applications of Foundation Models

• Introduction
• Task Statement 3.1: Describe design considerations for applications that use foundation models
• Task Statement 3.2: Choose effective prompt engineering techniques
• Task Statement 3.3: Describe the training and fine-tuning process for foundation models
• Task Statement 3.4: Describe methods to evaluate foundation model performance
• Walkthrough Questions
• Additional Resources

Domain 4: Guidelines for Responsible AI

• Introduction
• Task Statement 4.1: Explain the development of AI systems that are responsible
• Task Statement 4.2: Recognize the importance of transparent and explainable models
• Walkthrough Questions
• Additional Resources

Domain 5: Security, Compliance, and Governance for AI Solutions

• Introduction
• Task Statement 5.1: Explain methods to secure AI systems
• Task Statement 5.2: Recognize governance and compliance regulations for AI systems
• Walkthrough Questions
• Additional Resources

Module 4: Register for the Exam

Module 5: Course Close

Module 6: Course Survey