Domain 1: Fundamentals of AI and ML

Let's get started with Domain 1, which covers the fundamentals of artificial intelligence, AI, and machine learning, ML. Domain 1 is broken into three task statements that we will discuss over the next few lessons. 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.

For the first task statement, you must understand the basic concepts and terminology of artificial intelligence and its subcomponents: machine learning and deep learning. You will need to be able to describe, at a high level, how machine learning models work and are developed. This includes knowing the options for inferencing, the different types of data that are used in training models, and the main categories of learning algorithms.

For the second task statement, you must be familiar with some common AI use cases. These use cases include both when it's appropriate to use AI and when it might not be. For this task statement, there is also a requirement to know which ML technologies are appropriate for specific use cases. You will also need to know about the fully managed and pre-trained artificial intelligence and machine learning services that Amazon Web Services, AWS, provides.

For the third task statement, you'll describe the ML development lifecycle, which includes the components of an ML pipeline, from data collection to model hosting and monitoring. You'll identify the AWS services that can be used in each pipeline stage. Finally, you'll need to know how ML models are evaluated against performance and business metrics. Over the next few videos, I will address each task statement individually, breaking down each objective.

Domain 2: Fundamentals of GenAI

Let's get started with Domain 2, which covers the Fundamentals of Generative AI. For this domain, we will continue to talk about artificial intelligence and define generative AI. Domain 2 is broken into three tasks statements that we will discuss over the next few lessons. Task statement 2.1, explain the basic concepts of generative AI. Task statement 2.2, describe 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.

For the first task statement, to explain the basic concepts of generative AI, you must understand the basic definitions and differences between AI and generative AI. Also, ensure you understand the different use cases for generative AI models and the foundational model lifecycle.

The second task statement is to describe the capabilities and limitations of generative AI for solving business problems. You'll need to ensure that you understand how to use generative AI along with the potential advantages and risks. Also, ensure you understand different metrics and how to select the model to meet your requirements.

The third task statement is to describe AWS infrastructure and technologies for building generative AI applications. For this task, ensure that you understand the AWS services and features that you can use to develop generative AI applications. Under task statement 2.2, you need to ensure that you understand the advantages of generative AI. For this task, ensure you understand the advantages of using AWS infrastructure and AWS generative AI services and the cost tradeoffs to build your applications.

Over the next few videos, I will address each task statement individually, breaking down the knowledge and skills expected of you to be successful. Let's get started evaluating your readiness for the exam in the next lesson, where we will cover the first task statement from Domain 2. Task statement 2.1, explain the basic concepts of generative AI.

Domain 3: Applications of Foundation Models

Let's get started with Domain 3, which covers application of foundation models. For this domain, we will continue to talk about foundation models. Under Domain 2, we talked about foundation models and their lifecycle. Remember that foundation models are pre-trained models that are ready to use. They are trained on massive datasets. They are large, deep learning neural networks that provide a starting point to develop machine learning models that power new applications more quickly and cost effectively.

Let's ask a few questions. What is unique about foundation models? My first thought is adaptability, because these models can perform a wide range of tasks with a high degree of accuracy based on input prompts. Some tasks include natural language processing, NLP, question answering and image classification, but the size and general-purpose nature of foundation models also make them different from traditional machine learning models. Traditional ML models perform specific tasks like analyzing text for sentiment, classifying images, and forecasting trends. Here is another question.

What are applications for foundation models? Answer, customer support, language translation, content generation, code generation, copywriting, image classification, high-resolution image creation and editing, video and audio generation, document extraction, healthcare, autonomous vehicles, and robotics.

Domain 3 is broken into four task statements that we'll discuss over the next few lessons. 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.

The first task statement is to describe design considerations for applications that use foundation models. For this task, you will need to understand how to choose your pre-trained model and the effect of inference parameters on your model responses. Also, ensure that you can define retrieval augmented generation, or RAG, and describe its business application with Amazon Bedrock. This task statement also covers the cost tradeoffs to foundation model customizations such as RAG, pre-training, fine-tuning, and more. We will also talk about AWS services that store embeddings within vector databases and the role of agents in multi-step tasks.

The second task statement is to choose effective prompt engineering techniques. For this task statement, ensure that you understand the best practices, techniques, risks, and limitations of prompt engineering, and can describe the concepts and constructs of prompt engineering.

The third task statement is to describe the training and fine-tuning process for foundation models. For this task statement, you must understand the elements and methods for training a foundation model and how to prepare your data to fine-tune a foundation model.

The fourth task statement is to describe methods to evaluate foundation model performance. For this task, you must understand approaches and metrics to evaluate foundation model performance and how to determine whether the foundation model is meeting your business objectives.

Over the next few videos, I will address each task statement individually, breaking down the knowledge and skills expected of you to be successful. Let's get started evaluating your readiness for the exam in the next lesson where we will cover the first task statement from Domain 3.

Domain 4: Guidelines for Responsible AI

Let's get started with domain 4, which covers the guidelines for responsible AI. Domain 4 is broken into two task statements that we will discuss over the next few lessons.

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.

For the first task statement, you will need to understand the concept of responsible AI. You also must be able to identify the features and characteristics of responsible AI systems and how to use the tools that can help. You'll need to understand how responsible AI principles influence model selection, risk assessments, and dataset characteristics.

Finally, you must understand the concepts of bias and variance in the context of responsible AI. You'll need to understand the tools that you can use to monitor and detect bias and assess a model's trustworthiness and truthfulness.

For the second task statement, you'll need to understand a big challenge for responsible AI, which is the transparency and explainability of a model's inference. You'll need to understand what makes a model transparent or explainable, and tools that can be used to help explain a model's output. You must be able to identify the tradeoffs of a model safety as compared to its transparency.

Finally, you'll understand how human-centric design can help create AI that is more explainable.

Over the next few lessons, I will address each task statement individually breaking down each objective. Let's get started evaluating your readiness for the exam in the next lesson, where we will cover the first task statement from domain 4.

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

Let's get started with Domain 5, which covers security, compliance, and governance for AI solutions. Domain 5 is broken into two task statements that we will discuss over the next few lessons. Task statement 5.1. "Explain methods to secure AI systems." Task statement 5.2. "Recognize governance and compliance regulations for AI systems." For the first task statement, you must understand some basics about how identity and access management works on AWS. Also, understand how securing AI applications and data is a shared responsibility between AWS and the customer. You need to understand some ways in which AI systems are vulnerable to attack and theft, and be able to describe the best practices for mitigating them. For the second task statement, you'll need to understand some of the regulatory compliance standards for AI systems, be able to identify the AWS services, strategies, and processes that are used to meet them. Over the next few videos, I will address each task statement individually, breaking down each objective. Let's get started with evaluating your readiness for the exam in the next lesson, where we will cover the first task statement from Domain 5.

Conclusion

Welcome back, and great job completing this course. I hope you have enjoyed this exam prep course for the AWS Certified AI Practitioner certification. Throughout the course, we have provided valuable exam basics and guidance on how to approach the various domains from the exam guide that will be covered in the certification exam. As a reminder, this course was not intended to teach you what will be on the exam, but was intended to provide you a method for self-evaluation to determine your level of readiness to take the certification exam.

Use the information provided in this course to help guide you in your studies and preparations. Do not forget to get some hands-on experience too. AWS provides official practice exams for this certification. And similar to this course, practice exams will help you assess your readiness for the exam and highlight areas where you might still have gaps in your knowledge.

Let's cover a few test-taking tips. First, read and understand the question before looking at the answer options. Identify the keywords, phrases, and qualifiers. This is very important. If the question is looking for the lowest cost option and you are thinking of the most resilient solution, you might choose a different answer. Eliminate some of the answer options based on what you know about the topic. Compare and contrast the remaining options, keeping in mind the key phrases and qualifiers identified. If you are spending too much time, pick your best guess and flag the question for later review. You get zero points if you leave it blank. Remember that the AWS exam prep team also has other exam prep courses.

If you are looking for more in-depth and guided instructions and courses, you can also scan the QR code to find many great courses from AWS Training and Certification from the foundational, associate, professional, and specialty levels. For next steps, I recommend studying any areas that you have identified as gaps. When you are ready to take the test, visit our certification site to schedule your exam. Good luck with your studies and preparations and good luck on your exam. Feel free to reach out, and remember, AWS is here for you. We are all cheering for you. One last quick note, please complete our feedback survey. Feedback is so important to ensure we are creating content that you need. Good luck with your exam.