Exam Prep: AWS Certified AI Practitioner
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Foundations
On-Site, Virtual
1 day
This intermediate-level course prepares you for the AWS Certified AI Practitioner (AIF-C01) exam by providing a comprehensive exploration of the exam topics. You'll delve into the key areas covered on the exam, understanding how they relate to developing AI and machine learning solutions on the AWS platform. Through detailed explanations and walkthroughs of exam-style questions, you'll reinforce your knowledge, identify gaps in your understanding, and gain valuable strategies for tackling questions effectively. The course includes review of exam-style sample questions, to help you recognize incorrect responses and hone your test-taking abilities. By the end, you'll have a firm grasp on the concepts and practical applications tested on the AWS Certified AI Practitioner certification exam.
Course objectives
- Identify the scope and content tested by the AWS Certified AI Practitioner (AIF-C01) exam.
- Practice exam style questions and evaluate your preparation strategy.
- Examine use cases and differentiate between them.
Prerequisites
You are not required to take any specific training before taking this course. However, the following
prerequisite knowledge is recommended prior to taking the AWS Certified AI Practitioner (AIF-C01)
exam.
Recommended AWS knowledge
- Familiarity with the core AWS services (for example, Amazon EC2, Amazon S3, AWS Lambda,
and Amazon SageMaker AI) and AWS core services use cases. - Suggested to have up to 6 months of exposure to AI and ML technologies on AWS.
Intended Audience
This course is intended for individuals who are preparing for the AWS Certified AI Practitioner (AIF-C01)
exam.
Course outline
- 1.1: Explain basic AI concepts and terminologies
- 1.2: Identify practical use cases for AI
- 1.3: Describe the ML development lifecycle
- 2.1: Explain the basic concepts of generative AI
- 2.2: Understand the capabilities and limitations of generative AI for solving business problems
- 2.3: Describe AWS infrastructure and technologies for building generative AI applications
- 3.1: Describe design considerations for applications that use foundation models
- 3.2: Choose effective prompt engineering techniques
- 3.3: Describe the training and fine-tuning process for foundation models
- 3.4: Describe methods to evaluate foundation model performance
- 4.1: Explain the development of AI systems that are responsible
- 4.2: Recognize the importance of transparent and explainable models
- 5.1: Explain methods to secure AI systems
- 5.2: Recognize governance and compliance regulations for AI systems
