AWS Certified AI Practitioner (AIF-C01) - Exam Prep

Learn AI/ML, Generative AI, Amazon Bedrock, SageMaker, RAG, Prompt Engineering, Amazon Q and AWS AI Services
What you'll learn

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Understand the fundamentals of AI, Machine Learning, Deep Learning, and Neural Networks.
Learn supervised, unsupervised, and reinforcement learning concepts with practical examples.
Understand Generative AI, Foundation Models, LLMs, tokens, and context windows.
Learn prompt engineering, temperature, constraints, and techniques to improve AI outputs.
Explore Amazon Bedrock, SageMaker, SageMaker Canvas, SageMaker JumpStart, and Amazon Q.
Understand embeddings, vector databases, similarity search, and cosine similarity.
Learn Retrieval-Augmented Generation (RAG) and document grounding concepts.
Learn about AI risks including hallucinations, bias, toxicity, deepfakes, and how guardrails help mitigate them.
Prepare for the AWS Certified AI Practitioner (AIF-C01) exam through exam-focused concepts, examples, and practice questions.
Requirements

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Students should have an AWS account if they want to follow the hands-on AWS demonstrations.
Students should be interested in AI, Generative AI, Machine Learning, and AWS services.
Students preparing for the AWS Certified AI Practitioner (AIF-C01) certification can take this course directly.
Description

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AWS Certified AI Practitioner (AIF-C01) - Full Course
Prepare for the AWS Certified AI Practitioner (AIF-C01) certification while building a strong practical understanding of artificial intelligence, machine learning, generative AI, and AWS AI services.
This comprehensive 4+ hour course is designed to take you from the fundamentals of AI and machine learning to modern AWS AI technologies such as Amazon Bedrock, Amazon SageMaker, RAG, embeddings, foundation models, and Amazon Q.
Rather than focusing only on memorizing exam terminology, the course explains the concepts behind the technologies and connects them with AWS services, practical examples, hands-on demonstrations, and exam-style questions.
What You Will Learn

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In this course, you will learn how AI, machine learning, deep learning, and neural networks relate to each other and how models are trained and used for inference.
You will understand
AI, machine learning, deep learning, and neural networks
Training vs. inference
Batch vs. real-time inference
Labeled, unlabeled, structured, unstructured, and semi-structured data
Supervised, unsupervised, and reinforcement learning
Classification, regression, clustering, and other machine learning concepts
Practical machine learning workflows using Amazon SageMaker Canvas
The course then moves into Generative AI and foundation model concepts, including
Generative AI fundamentals
Large Language Models and foundation models
Tokens and tokenization
Context windows
Prompt engineering
Temperature and model parameters
Constraints and improving model consistency
Hallucinations, toxicity, bias, and other limitations
Foundation model selection
Amazon Bedrock
Amazon SageMaker JumpStart
Amazon Q
You will also learn the concepts behind Retrieval-Augmented Generation and document grounding
Embeddings
Vector representations
Similarity search
Cosine similarity
Vector databases
Retrieval-Augmented Generation (RAG)
Document grounding
Knowledge bases
Retrieval workflows
AWS-based RAG architecture
The course includes practical demonstrations showing how these concepts can be applied using AWS services. For example, the RAG section demonstrates a small document-grounding workflow using an Amazon Bedrock embeddings model and similarity search.
You will also explore AI image generation and diffusion models, including how generative AI systems can produce images and the responsible use of AI-generated content.
The course also covers responsible AI concepts such as
Bias and fairness
Model limitations
Hallucination mitigation
Toxicity
Responsible image generation
Deepfakes and impersonation
Copyright and intellectual property considerations
Guardrails and safety controls
Reviewing and validating AI outputs
These topics are especially important because responsible AI is part of the AWS AI Practitioner exam objectives.
Finally, you will learn important AWS security, compliance, governance, and cost-related concepts relevant to AI workloads, helping you understand not only individual AWS services but also how different AWS capabilities fit together.
Hands-On Learning
This is not a purely theoretical course.
Throughout the course, concepts are connected to practical AWS demonstrations. You will see examples using services such as Amazon SageMaker Canvas and Amazon Bedrock.
For example, the SageMaker section demonstrates supervised and unsupervised learning using a practical dataset and shows how a model can be trained and used for prediction.
You will also explore Amazon Bedrock, embeddings, document grounding, similarity search, RAG concepts, image generation, and Amazon Q through practical demonstrations and examples.
Exam Preparation
The course is structured with the AWS Certified AI Practitioner (AIF-C01) certification in mind.
You will encounter exam-focused explanations, practice questions, scenario-based thinking, summaries, and tips designed to help you recognize the concepts and AWS services commonly tested in certification-style questions.
The goal is not simply to memorize definitions. You will learn how to understand the scenario, identify the relevant AI/ML or Generative AI concept, connect it to the appropriate AWS service, and reason through the available options.
Who Is This Course For?
This course is suitable for
Beginners who want to understand AI and machine learning on AWS
Cloud professionals moving into AI and Generative AI
Developers and engineers working with AWS
IT professionals preparing for the AWS Certified AI Practitioner certification
AWS learners who want to understand Amazon Bedrock and modern AWS AI services
Anyone looking for a structured introduction to AI, ML, Generative AI, and AWS AI technologies
You do not need to be an advanced machine learning engineer to follow the course. The concepts are explained progressively, starting with the fundamentals and moving toward Generative AI, RAG, foundation models, and AWS AI services.
Course Approach
The learning approach combines
Conceptual explanations
AWS service mapping
Practical demonstrations
Real-world examples
Exam-focused tips
Practice questions and knowledge checks
This approach is designed to help you understand both the certification concepts and how the technologies are used in real AWS environments.
By the end of the course, you should have a strong foundation in AWS AI and machine learning concepts, a practical understanding of key AWS AI services, and a structured preparation path for the AWS Certified AI Practitioner (AIF-C01) exam.
Start learning today and build your foundation in AWS AI, machine learning, Generative AI, and modern AI applications.
Who this course is for

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Beginners who want to learn AI, Machine Learning, and Generative AI on AWS.
IT professionals preparing for the AWS Certified AI Practitioner (AIF-C01) certification.
AWS Cloud professionals who want to expand their knowledge into AI and Machine Learning.
Developers and engineers interested in building AI-powered applications using AWS services.
Cloud and DevOps professionals looking to understand modern AWS AI and Generative AI services.
Students and technology professionals who want to understand Generative AI and Foundation Models.
Learners who want to understand Amazon Bedrock, SageMaker, SageMaker Canvas, SageMaker JumpStart, and Amazon Q.
Anyone looking for a structured combination of AWS AI Practitioner exam preparation and practical AI knowledge.
Homepage

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