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AWS Certified AI Practitioner (AIF-C01) - Exam Prep
Published 8/2026
Created by EduVerse Academy
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 12 Lectures ( 4h 9m ) | Size: 1.9 GB
Learn AI/ML, Generative AI, Amazon Bedrock, SageMaker, RAG, Prompt Engineering, Amazon Q and AWS AI Services
What you'll learn
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
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
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
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 networksTraining vs. inferenceBatch vs. real-time inferenceLabeled, unlabeled, structured, unstructured, and semi-structured dataSupervised, unsupervised, and reinforcement learningClassification, regression, clustering, and other machine learning conceptsPractical machine learning workflows using Amazon SageMaker CanvasThe course then moves into Generative AI and foundation model concepts, including
Generative AI fundamentalsLarge Language Models and foundation modelsTokens and tokenizationContext windowsPrompt engineeringTemperature and model parametersConstraints and improving model consistencyHallucinations, toxicity, bias, and other limitationsFoundation model selectionAmazon BedrockAmazon SageMaker JumpStartAmazon QYou will also learn the concepts behind Retrieval-Augmented Generation and document grounding
EmbeddingsVector representationsSimilarity searchCosine similarityVector databasesRetrieval-Augmented Generation (RAG)Document groundingKnowledge basesRetrieval workflowsAWS-based RAG architectureThe 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 fairnessModel limitationsHallucination mitigationToxicityResponsible image generationDeepfakes and impersonationCopyright and intellectual property considerationsGuardrails and safety controlsReviewing and validating AI outputsThese 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 AWSCloud professionals moving into AI and Generative AIDevelopers and engineers working with AWSIT professionals preparing for the AWS Certified AI Practitioner certificationAWS learners who want to understand Amazon Bedrock and modern AWS AI servicesAnyone looking for a structured introduction to AI, ML, Generative AI, and AWS AI technologiesYou 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 explanationsAWS service mappingPractical demonstrationsReal-world examplesExam-focused tipsPractice questions and knowledge checksThis 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
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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