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Complete AWS Certified AI Practitioner – AIF-C01 [NEW]

Nikolai Schuler

This course will prepare you to PASS the new AWS Certified AI Practitioner (AIF-C01) certification exam.

No previous experience with AWS or AI is needed—this course is beginner-friendly.

This can be the big next step in your career with the AWS Certified AI Practitioner certification.

Whether you’re new to AI or looking to expand your AWS expertise, this course offers everything you need—practical labs, a full practice exam, and up-to-date content that covers every aspect of AI, ML, and Generative AI on AWS.

Read more

This course will prepare you to PASS the new AWS Certified AI Practitioner (AIF-C01) certification exam.

No previous experience with AWS or AI is needed—this course is beginner-friendly.

This can be the big next step in your career with the AWS Certified AI Practitioner certification.

Whether you’re new to AI or looking to expand your AWS expertise, this course offers everything you need—practical labs, a full practice exam, and up-to-date content that covers every aspect of AI, ML, and Generative AI on AWS.

Why is this the ONLY course you need to pass the AWS Certified AI Practitioner exam?

  • Comprehensive Coverage: Every topic you need to master the AIF-C01 exam is covered in depth (based on all the latest information).

  • 100% Up-to-Date: Content is continuously updated to reflect the latest exam changes and AWS services.

  • Hands-on & Practical: Learn in hands-on labs how to use all the relevant AWS AI services like SageMaker, Bedrock, and Comprehend and many more.

  • Quizzes: Challenge and test your knowledge with lots of quizzes through out all the sections to be sure of your knowledge.

  • Full Practice Exam: Test your readiness with a comprehensive practice exam, complete with detailed explanations for every question.

  • Expert Tips: Learn the best strategies to approach the exam, manage your time, and avoid common pitfalls.

This course doesn’t just prepare you for the exam—it equips you with the practical knowledge to use AI and ML in real-world AWS environments. You’ll walk away with the skills and confidence needed to excel in your career.

In short, this course teaches you every single topic you need to master the exam with ease.

What You’ll Learn:

  • Pass the AIF-C01 Exam: The only course you need to become an AWS Certified AI Practitioner.

  • Master AI & ML on AWS: Learn how to use key AWS AI services to build and deploy AI solutions.

  • Hands-on Projects: Apply what you learn in practical labs, covering everything from model training to deploying AI systems.

  • In-Depth Understanding: Get a thorough grounding in AI/ML fundamentals, generative AI, prompt engineering, and responsible AI practices.

  • Exam Strategies: Gain insights and tips to tackle the exam confidently and efficiently.

Enroll Now and Get:

  • Lifetime Access including all future updates

  • Several hours of video content

  • All slides & project files as downloadable resources

  • Full practice exam with explanations

  • 30-day money-back guarantee with no-questions-asked

Your instructor:

Hi, my name is Nikolai. I am teaching AWS, Data Engineering and Data Science in 200 countries and my mission with the course: Take the stress out of your exam prep, make it fun but very effective to make the most out of your preparation time. I want to make sure you have the best chances of succeeding and moving your career forward with the AWS AI Practitioner exam in your professional career.

Take this step today, and start your journey to becoming an AWS Certified AI Practitioner.

Looking forward to seeing you inside the course.

Enroll now

What's inside

Learning objectives

  • Pass the aws certified ai practitioner exam (aif-c01)
  • Full practice exam with complete explanations to ace the exam
  • All slides available as downloadable pdfs
  • 100% up-to-date with the latest content
  • Hands-on demos with real-world scenarios
  • Practical & hands-on
  • Develop & deploy ai solutions using aws services like sagemaker and bedrock
  • All relevant ai services in aws are covered in hands-on demos

Syllabus

Introduction
Welcome!
About the exam & this course
Important tips for this course
Read more
All Slides
AWS Free Trial Signup
Cost & AWS Budget
Generative AI & ML - Fundamentals
Amazon Q Business
What is Amazon Q Business
Hands-on: Create Amazon Q Business Application
Hands-on: Assign Users & Test Application
Hands-on: Setup a Knowledge Base
Hands-on: Using Global Controls
Hands-on: Blocking Words
Hands-on: Topic Controls
Hands-on: Creating Q Apps
Hands-on: Clean-up Resoures
Language AI Services
Amazon Transcribe
Hands-on: Amazon Transcribe
Amazon Polly
Hands-on: Pricing & Models (Amazon Polly)
Hands-on: Cleaning up resources
Hands-on: Text-to-Speech (Amazon Polly)
Hands-on: SSML to modify speech output (Amazon Polly)
Hands-on: Real-time translation (Amazon Translate)
Hands-on: Batch job translation (Amazon Translate)
Generative AI: Selection and Metrics
Diffusion Models
Large Language Models
GANs & VAEs
Generative AI Capabilities
Generative AI Challenges
Selecting Generative AI Model
Business Metrics
Generative AI Lifecycle
Amazon Bedrock
Prompt Engineering
What is Amazon Bedrock?
Amazon Bedrock - Architecture
Amazon Bedrock - Use Cases
Hands-on: Exploring Amazon Bedrock
Hands-on: Installing Visual Studio Code
Hands-on: Setting up Visual Studio Code
Hands-on: Invoking Amazon Titan Model
Hands-on: Image Generation in Bedrock
Improving Foundation Models
Essentials of Prompt Engineering
Why Improving Foundation Models?
Retrieval-Augmented Generation (RAG)
Hands-on: Knowledge Base in Bedrock
Agents
Hands-on: Training Dataset (Amazon Personalize)
Hands-on: Setting up Agent
Hands-on: Prepare & Test Agent
Hands-on: Create Agent Action Group
Hands-on: Creating Guardrails
Hands-on: Testing Guardrails
Optimizing Inference Parameters
Prompting Best Practices
Prompt Engineering Techniques
Prompt Misuses and Risks
Amazon Comprehend
Hands-on: Train Model (Amazon Personalize)
Hands-on: Amazon Comprehend
Hands-on: Amazon Comprehend Medical
Augmented Analysis
Amazon Textract
Hands-on: Amazon Textract
Computer Vision
Amazon Rekognition
Hands-on: Amazon Rekognition
Hands-on: Using Rekognition in Lambda Function
Customer Experience
Amazon Kendra
Hands-on: Create an Index & Sync (Amazon Kendra)
Hands-on: Create Experience (Amazon Kendra)
Amazon Personalize
Hands-on: Dataset Group (Amazon Personalize)
Hands-on: Make Predictions (Amazon Personalize)
Developing Machine Learning Solutions
Machine Learning Development Lifecycle
SageMaker: Data Collection & Feature Engineering
SageMaker: Model Training

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Offers comprehensive coverage of topics needed to master the AIF-C01 exam, ensuring learners are well-prepared for certification
Provides hands-on labs using AWS AI services like SageMaker and Bedrock, enabling learners to apply their knowledge in practical scenarios
Includes a full practice exam with detailed explanations, allowing learners to assess their readiness and identify areas for improvement
Requires learners to sign up for an AWS Free Tier account, which may involve providing payment information even if intending to stay within the free tier limits
Teaches how to use Amazon Q Business, which may not be relevant to learners who are not interested in developing AI-powered business applications
Covers Generative AI topics such as GANs and VAEs, which may be rapidly evolving and subject to change, potentially requiring learners to stay updated on the latest advancements

Save this course

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete AWS Certified AI Practitioner – AIF-C01 [NEW] with these activities:
Review Machine Learning Fundamentals
Solidify your understanding of core machine learning concepts before diving into AWS-specific implementations. This will help you better grasp the underlying principles behind the AWS AI services.
Browse courses on Machine Learning Fundamentals
Show steps
  • Review key concepts like supervised and unsupervised learning.
  • Brush up on common algorithms like linear regression and decision trees.
  • Familiarize yourself with model evaluation metrics.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'
Gain a deeper understanding of machine learning algorithms and techniques. This book will help you build a solid foundation for using AWS AI services.
View Hands-On Machine Learning with Scikit-Learn,... on Amazon
Show steps
  • Read the chapters on supervised and unsupervised learning.
  • Work through the code examples to gain hands-on experience.
  • Focus on the sections related to model evaluation and hyperparameter tuning.
Build a Simple Image Classifier with SageMaker
Apply your knowledge of SageMaker by building a simple image classifier. This hands-on project will reinforce your understanding of the ML development lifecycle on AWS.
Show steps
  • Gather a small dataset of images (e.g., from Kaggle).
  • Use SageMaker to train an image classification model.
  • Deploy the model to an endpoint and test its performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Prompt Engineering Exercises
Improve your prompt engineering skills for Amazon Bedrock. Practice crafting effective prompts to get the desired outputs from foundation models.
Show steps
  • Experiment with different prompting techniques (e.g., few-shot learning).
  • Try different prompt structures and wording.
  • Evaluate the outputs and refine your prompts accordingly.
Follow AWS AI Service Tutorials
Enhance your practical skills by following guided tutorials for various AWS AI services. This will expose you to different use cases and implementation techniques.
Show steps
  • Choose a tutorial for an AWS AI service you want to learn more about (e.g., Amazon Comprehend).
  • Follow the tutorial step-by-step, paying attention to the details.
  • Experiment with the code and adapt it to your own use cases.
Write a Blog Post on Responsible AI on AWS
Deepen your understanding of responsible AI practices by researching and writing a blog post. This will help you articulate the ethical considerations of AI and how to address them on AWS.
Show steps
  • Research the key principles of responsible AI.
  • Explore AWS services and features that support responsible AI.
  • Write a blog post summarizing your findings and offering practical advice.
Read 'Generative AI with Python and TensorFlow 2'
Expand your knowledge of generative AI models and techniques. This book will provide a solid foundation for understanding and using generative AI services on AWS.
View Generative Deep Learning on Amazon
Show steps
  • Read the chapters on GANs, VAEs, and diffusion models.
  • Work through the code examples to gain hands-on experience with generative AI models.
  • Experiment with different model architectures and hyperparameters.

Career center

Learners who complete Complete AWS Certified AI Practitioner – AIF-C01 [NEW] will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models. This course provides exposure to key Amazon Web Services such as SageMaker, Bedrock, and Comprehend which are essential for creating practical AI solutions, thereby building a foundation for a career as a machine learning engineer. Hands-on labs in the course, covering model training and deployment, will allow one to gain practical expertise. The course's focus on practical application of AI and machine learning in AWS environments makes it uniquely suited for those aspiring to work as machine learning engineers.
See salaries and explore the career path for Machine Learning Engineer
Artificial Intelligence Specialist
An Artificial Intelligence Specialist focuses on developing and implementing AI solutions. This course provides a comprehensive overview of AI and machine learning, especially in the AWS ecosystem, which is highly relevant to a career as an artificial intelligence specialist. Furthermore, the course offers in-depth coverage of generative AI, prompt engineering, and responsible AI practices, all of which are crucial for this field. The hands-on use of AWS AI services ensures practical skills development, allowing one to become better suited as an artificial intelligence specialist.
See salaries and explore the career path for Artificial Intelligence Specialist
AI Software Developer
An AI Software Developer creates software applications using artificial intelligence and machine learning. This course directly aligns with the kind of work performed by AI software developers through its coverage of AI and ML on AWS using services like SageMaker, Bedrock, and Comprehend, and offers practical labs to build and deploy AI solutions. This course provides the necessary foundation for anyone who is looking to start a career as an AI software developer. The practical emphasis of this course helps one quickly gain relevant experience.
See salaries and explore the career path for AI Software Developer
Cloud Solutions Architect
A Cloud Solutions Architect designs and implements cloud-based solutions. This course helps build a strong foundation in AWS AI services such as SageMaker and Bedrock along with other services relevant to the role of a cloud solutions architect developing AI integrated applications. The course offers a mix of theoretical knowledge and hands-on experience that would be essential in order to design and implement solutions. This course's focus on AWS AI services makes it an excellent resource for aspiring cloud solutions architects looking to specialize in AI related cloud applications.
See salaries and explore the career path for Cloud Solutions Architect
Machine Learning Operations Engineer
A Machine Learning Operations Engineer is responsible for deploying, maintaining, and monitoring machine learning models in production. This course helps someone who is interested in becoming a machine learning operations engineer by providing a solid grounding in key AWS AI services and their practical applications. By learning how to deploy and manage AI systems on AWS, you can gain the necessary skills. The hands-on practical labs in the course will allow you to develop proficiency and understand how to perform real-world tasks in this role.
See salaries and explore the career path for Machine Learning Operations Engineer
Cloud Engineer
A Cloud Engineer implements, manages, and supports cloud computing infrastructure. This course can be a stepping stone for aspiring cloud engineers, especially those looking to work with AWS AI and ML services since it covers key services and their practical applications. The course focuses on hands-on labs with practical scenarios, which will help a cloud engineer be more effective in their daily work. The combination of theoretical knowledge and practical labs makes it valuable to those who want to work in this field.
See salaries and explore the career path for Cloud Engineer
Solutions Engineer
A Solutions Engineer focuses on creating technical solutions to customer problems. This course will be helpful for an aspiring solutions engineer who wants to gain experience with AWS AI and machine learning, and specifically through the practical labs. The course covers core AWS AI services and their usage, which is directly useful for a solutions engineer working with AWS based applications. This course could provide the ability for an engineer to design impactful solutions by understanding the different AWS services available.
See salaries and explore the career path for Solutions Engineer
AI Consultant
An AI Consultant advises clients on how to use artificial intelligence to achieve business goals. This course's focus on practical applications of AI and machine learning in AWS helps an AI consultant. This course will cover the knowledge necessary in order to offer insightful advice and strategy. The course also covers responsible AI practices, which are useful for consultants who need to consider long term impacts. Those who wish to become an AI consultant will benefit from taking this course.
See salaries and explore the career path for AI Consultant
Data Scientist
A Data Scientist analyzes data to extract meaningful insights and build predictive models. This course may be useful for those looking to leverage AWS AI tools, because it covers machine learning fundamentals, generative AI, and practical implementation using AWS services. While this course is not solely focused on data science, those interested in how AI and ML work in cloud environments will find it beneficial. By learning about model training and deployment in the AWS ecosystem, one can become a more efficient and effective data scientist.
See salaries and explore the career path for Data Scientist
Software Architect
A Software Architect designs the structure of complex software systems. This course can be useful for those looking to work in a software architect role, as it provides insight into practical applications of AI and machine learning using AWS services. The course is not exclusively focused on software architecture, but it does provide a hands-on experience with AI in AWS environments which can be relevant for designing systems that involve AI and ML. The knowledge acquired in this course may be useful when creating systems that rely on AWS services.
See salaries and explore the career path for Software Architect
Data Analyst
A Data Analyst interprets data and transforms it into actionable points for decision making. This course may be useful for data analysts by providing experience with AWS tools. This course provides a broad intro to AI and ML and practical application with AWS services that may be useful. The course is especially valuable for a data analyst who is looking to improve their skills and expand the scope of their work.
See salaries and explore the career path for Data Analyst
Research Scientist
A Research Scientist conducts research to advance new technologies and scientific understanding. This course may be useful for research scientists by introducing them to AI and machine learning concepts within the AWS ecosystem. This course can help a research scientist build familiarity with the tools and technologies, such as Bedrock and SageMaker, needed to implement cutting edge research ideas within a cloud based environment. The course is especially valuable for those who are working on or considering working on AI related research.
See salaries and explore the career path for Research Scientist
Business Intelligence Analyst
A Business Intelligence Analyst analyzes data to provide insights that impact business decisions. This course may be beneficial for business intelligence analysts by providing an introduction to AI and machine learning concepts as implemented in the AWS environment. Even though the course is primarily focused on AI implementation, a business intelligence analyst may leverage the knowledge gained in this course to better understand the tools used to create the data they work with. The knowledge may also be valuable for understanding how AI can be leveraged to create more effective analysis for a business.
See salaries and explore the career path for Business Intelligence Analyst
Technical Project Manager
A Technical Project Manager oversees the planning, execution, and delivery of technical projects. This course may be useful for a technical project manager by providing an overview of AI and ML using AWS services. This course will allow a technical project manager to better understand what their team members do, and make better decisions. This is especially true for project managers who are working on AI related projects within AWS environments.
See salaries and explore the career path for Technical Project Manager
Technical Writer
A Technical Writer develops documentation for technical products and services. This course may be useful for a technical writer who wishes to gain familiarity with the AI/ML services offered by AWS. It is possible the knowledge gained in this course will help inform the documentation that is written. The course can help a technical writer generate more effective and informed content for technical audiences.
See salaries and explore the career path for Technical Writer

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Complete AWS Certified AI Practitioner – AIF-C01 [NEW].
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a comprehensive introduction to machine learning concepts and tools, including Scikit-Learn, Keras, and TensorFlow. It's particularly useful for understanding the practical aspects of model building and training, which are essential for using AWS SageMaker effectively. While not AWS-specific, it provides a strong foundation for understanding the underlying ML principles. This book is commonly used as a textbook at academic institutions.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Generative Deep Learning
Save
Provides a deep dive into generative AI models using Python and TensorFlow 2. It covers various generative models, including GANs, VAEs, and diffusion models, which are relevant to the Generative AI section of the AWS Certified AI Practitioner exam. While the book doesn't focus on AWS specifically, it provides a strong theoretical and practical foundation for understanding and implementing generative AI models that can be deployed on AWS. This book is more valuable as additional reading than it is as a current reference.
Generative Deep Learning
Paperback
Check
Generative Deep Learning
Kindle Edition
Check

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Effort
11 total hours
Via
Udemy
Instructor
Nikolai Schuler
Language
English
Good to know
Offers comprehensive coverage of topics needed to master the AIF-C01 exam, ensuring learners are well-prepared for certification
Provides hands-on labs using AWS AI services like SageMaker and Bedrock, enabling learners to apply their knowledge in practical scenarios
Includes a full practice exam with detailed explanations, allowing learners to assess their readiness and identify areas for improvement
Requires learners to sign up for an AWS Free Tier account, which may involve providing payment information even if intending to stay within the free tier limits
Teaches how to use Amazon Q Business, which may not be relevant to learners who are not interested in developing AI-powered business applications
Covers Generative AI topics such as GANs and VAEs, which may be rapidly evolving and subject to change, potentially requiring learners to stay updated on the latest advancements
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