Guide for clearing AWS Certified AI Practitioner (AIF-C01) exam

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Section wise Summary

• Doing Recap/Want to have short guide? Directly go through the following summaries of each section or at Summary section at end.
  • 1. Fundamentals of ML and AI
  • 7. Domain 4 - Responsible AI Practices
  • 8. Domain 5 - Security, Compliance, and Governance for AI Solutions

Exam topics

Domain 1: Fundamentals of AI and ML (20% of scored content)

1. Explain basic AI concepts and terminologies.
   • Define basic AI terms.
   • Describe the similarities and differences between AI, ML, and deep learning.
   • Describe various types of inferencing.
   • Describe the different types of data in AI models.
   • Describe supervised learning, unsupervised learning, and reinforcement learning.
2. Identify practical use cases for AI.
   • Recognize applications where AI/ML can provide value.
   • Determine when AI/ML solutions are not appropriate.
   • Select the appropriate ML techniques for specific use cases.
   • Identify examples of real-world AI applications.
   • Explain the capabilities of AWS managed AI/ML services.
3. Describe the ML development lifecycle.
   • Describe components of an ML pipeline.
   • Understand sources of ML models.
   • Describe methods to use a model in production.
   • Identify relevant AWS services and features for each stage of an ML pipeline.
   • Understand fundamental concepts of ML operations (MLOps).
   • Understand model performance metrics.

Domain 2: Fundamentals of Generative AI (24% of scored content)

1. Explain the basic concepts of generative AI.
   • Understand foundational generative AI concepts.
   • Identify potential use cases for generative AI models.
   • Describe the foundation model lifecycle.
2. Understand the capabilities and limitations of generative AI for solving business problems.
   • Describe the advantages of generative AI.
   • Identify disadvantages of generative AI solutions.
   • Understand various factors to select appropriate generative AI models.
   • Determine business value and metrics for generative AI applications.
3. Describe AWS infrastructure and technologies for building generative AI applications.
   • Identify AWS services and features to develop generative AI applications.
   • Describe the advantages of using AWS generative AI services to build applications.
   • Understand the benefits of AWS infrastructure for generative AI.
   • Understand cost tradeoffs of AWS generative AI services.

Domain 3: Applications of Foundation Models (28% of scored content)

1. Describe design considerations for applications that use foundation models.
   • Identify selection criteria to choose pre-trained models.
   • Understand the effect of inference parameters on model responses.
   • Define Retrieval Augmented Generation (RAG) and describe its business applications.
   • Identify AWS services that help store embeddings within vector databases.
   • Explain the cost tradeoffs of various approaches to foundation model customization.
   • Understand the role of agents in multi-step tasks.
2. Choose effective prompt engineering techniques.
   • Describe the concepts and constructs of prompt engineering.
   • Understand techniques for prompt engineering.
   • Understand the benefits and best practices for prompt engineering.
   • Define potential risks and limitations of prompt engineering.
3. Describe the training and fine-tuning process for foundation models.
   • Describe the key elements of training a foundation model.
   • Define methods for fine-tuning a foundation model.
   • Describe how to prepare data to fine-tune a foundation model.
4. Describe methods to evaluate foundation model performance.
   • Understand approaches to evaluate foundation model performance.
   • Identify relevant metrics to assess foundation model performance.
   • Determine whether a foundation model effectively meets business objectives.

Domain 4: Guidelines for Responsible AI (14% of scored content)

1. Explain the development of AI systems that are responsible.
   • Identify features of responsible AI.
   • Understand how to use tools to identify features of responsible AI.
   • Understand responsible practices to select a model.
   • Identify legal risks of working with generative AI.
   • Identify characteristics of datasets.
   • Understand effects of bias and variance.
   • Describe tools to detect and monitor bias, trustworthiness, and truthfulness.
2. Recognize the importance of transparent and explainable models.
   • Understand the differences between models that are transparent and explainable and models that are not transparent and explainable.
   • Understand the tools to identify transparent and explainable models.
   • Identify tradeoffs between model safety and transparency.
   • Understand principles of human-centered design for explainable AI.

Domain 5: Security, Compliance, and Governance for AI Solutions (14% of scored content)

1. Explain methods to secure AI systems.
   • Identify AWS services and features to secure AI systems.
   • Understand the concept of source citation and documenting data origins.
   • Describe best practices for secure data engineering.
   • Understand security and privacy considerations for AI systems.
2. Recognize governance and compliance regulations for AI systems.
   • Identify regulatory compliance standards for AI systems.
   • Identify AWS services and features to assist with governance and regulation compliance.
   • Describe data governance strategies.
   • Describe processes to follow governance protocols.

  This guide is an improved version inspired by the AWS Skillbuilder Learning Path Standard Exam Prep Plan: AWS Certified AI Practitioner (AIF-C01).