AWS Certified AI Practitioner (AIF-C01)
Course Curriculum
16 modules designed to master the subject.
Module 1: Exam Orientation and AI Fundamentals
Understand the certification structure, exam domains, and how this fits into your AWS career path.
Certification Overview and Exam Structure
Everything you need to know about the AWS Certified AI Practitioner (AIF-C01) exam. From registration to the final question, we break down the logistics of success.
The Battle Map: Exam Domains and Weightings
A strategic breakdown of the five key domains of the AIF-C01 exam. Learn where to focus your study time for maximum impact.
Winning the Game: Question Types and Scoring Strategy
Master the art of the AWS exam. Learn how to decode questions, avoid distractors, and understand how the scaled scoring engine works.
The Badge of Literacy: What this Certification Validates
Why the AI Practitioner is different from a Data Science cert. Learn the core competencies you are proving to the world.
The Career Ladder: AI Certification Paths
Where do you go from here? Map your career trajectory from AI Practitioner to Architect, Engineer, or Strategic Leader.
Module 2: Core AI Concepts
Learn the fundamentals of Artificial Intelligence, Machine Learning types, and common terminology.
The Intelligence Evolution: What is Artificial Intelligence?
Demystifying AI from first principles. Learn the difference between human-like 'Thinking' and mathematical 'Modeling' in the cloud.
The Scope of Intelligence: Narrow AI vs. General AI
Why we aren't in a sci-fi movie yet. Learn the difference between specialized 'Narrow' AI and the theoretical 'General' AI.
The Logic Shift: Machine Learning vs. Rule-Based Systems
Why we stopped writing 'If/Then' statements. Learn how Machine Learning flips the script on traditional programming.
The Three Schools of Learning: Supervised, Unsupervised, and Reinforcement
How does a machine actually learn? Master the three core methodologies that power almost every AI system on AWS.
The Practitioner's Dictionary: Common Terminology Explained
Stop being confused by jargon. Master the essential terms from 'Weights' to 'Hallucinations' that will appear on your exam.
Module 3: Generative AI Fundamentals
Deep dive into Large Language Models (LLMs), multimodal generation, and GenAI limitations.
The Creative Machine: What is Generative AI?
More than just a chatbot. Learn how Generative AI shifted the world from 'Classifying' data to 'Creating' it.
The Engine of Words: Large Language Models (LLMs)
How computers learned to speak. Deep dive into the mechanics of LLMs, Transformers, and Tokens.
Beyond Words: Image and Multimodal Models
How AI draws and sees. Understand the concepts behind Diffusion models and the power of Multimodal AI.
AI in Action: Common Generative AI Use Cases
From Lab to Boardroom. Explore the high-impact use cases where GenAI is creating real business value today.
The Reality Check: Limitations of Generative AI
Why AI isn't perfect. Master the risks of Hallucinations, Data Cutoffs, and the sheer cost of being 'Creative'.
Module 4: AWS AI Service Landscape
Navigate the AWS AI vs ML offerings and learn when to use managed services vs custom models.
The Three Kingdoms: AI, ML, and Analytics on AWS
Navigate the AWS catalog like a pro. Learn the difference between the AI 'Pre-trained' realm and the ML 'Build-your-own' realm.
The Great Debate: Managed AI vs. Custom ML
Control vs. Convenience. Learn the strategic framework for choosing between a 'Ready-to-use' API and a 'Build-it-yourself' SageMaker model.
The Selection Matrix: Choosing Your AI Service
Master the 'Match Game'. Learn the heuristics and indicators that point to the correct AWS AI service for every scenario.
Module 5: AWS AI Services (Pre-Trained)
Master Amazon Rekognition, Comprehend, Transcribe, Polly, and Textract for business scenarios.
The Digital Eye: Amazon Rekognition
Master vision in the cloud. Explore image and video analysis, face recognition, and the powerful 'Custom Labels' feature.
Understanding Meaning: Amazon Comprehend
Master Natural Language Processing (NLP). Learn how to extract sentiment, entities, and PII from raw text and specialized medical data.
Voices in the Cloud: Amazon Transcribe and Polly
Master the audio domain. Learn how to convert spoken words to text with Transcribe and give your apps a voice with Polly.
The Data Extractor: Amazon Textract
Beyond OCR. Learn how Textract understands the structure of forms, tables, and handwritten documents to turn paper into database-ready data.
The Master Scenario Matrix: AI Services in the Real World
Prepare for situational questions. Learn the end-to-end combinations of AWS AI services that solve complex business problems.
Module 6: Amazon Bedrock and Generative AI on AWS
Explore foundation models, Amazon Bedrock, and high-level GenAI integration patterns.
The Portal to Generative AI: Amazon Bedrock
More than just a model. Learn why Amazon Bedrock is the essential platform for building enterprise-grade Generative AI applications.
The Deep Dive: Foundation Model Concepts
Master the architecture of the future. Learn how Foundation Models are built, trained, and why they represent a paradigm shift in AI.
The Creative Palette: Generation Use Cases on Bedrock
From stunning art to complex logic. Experience the real-world applications of Amazon Bedrock's multi-modal foundation models.
The Customization Spectrum: RAG and Fine-Tuning
How to make AI an expert on your data. Explore Retrieval-Augmented Generation (RAG) and Fine-Tuning in Amazon Bedrock.
The Enterprise Advantage: Fully Managed Foundation Models
Why 'Managed' is the best way to GenAI. Discover the operational, economic, and security benefits of using Amazon Bedrock.
Module 7: Amazon SageMaker (High-Level View)
Learn the role of SageMaker in the AI lifecycle, from training to deployment and no-code options.
The Workhorse of ML: What is Amazon SageMaker?
Meet the end-to-end platform for machine learning. Learn why SageMaker is the go-to tool for building, training, and deploying custom models.
The Lifecycle Phases: Training vs. Inference
From learning to predicting. Understand the distinct phases of the machine learning lifecycle and their impact on cost and performance.
The Strategic Threshold: When to Use SageMaker
Avoid over-engineering. Learn the specific business and technical indicators that prove you need the power of SageMaker.
AI for Everyone: No-Code and Low-Code ML
Build models without writing a single line of Python. Explore SageMaker Canvas and the democratized side of Machine Learning.
Module 8: AI for Business Applications
Apply AI to customer support, content generation, search, and decision support systems.
Transforming Conversation: AI for Customer Support
From 'Press 1' to 'I can help'. Learn how Amazon Lex and Amazon Connect use AI to revolutionize the customer experience.
The Segment of One: AI for Personalization
Move beyond generic marketing. Learn how Amazon Personalize and Bedrock create unique experiences for every customer.
The Intelligent Librarian: AI for Document Search
Unlock the data trapped in your PDFs. Learn how Amazon Kendra and Amazon Q transform company documents into interactive knowledge.
Predicting the Future: AI for Decision Support
Master the math of time. Learn how Amazon Forecast and Fraud Detector help business leaders see what's coming and stop what shouldn't happen.
Module 9: AI Adoption Strategy
Analyze build vs buy decisions, calculate AI ROI, and identify high-impact opportunities.
The Strategic Fork: Build vs. Buy Decisions
Master the economics of AI. Learn the deep-dive factors that define when to buy a managed API and when to build a custom SageMaker model.
The Impact Filter: Identifying AI Opportunities
Don't build AI for the sake of AI. Learn how to filter the hype and find the projects that move the needle for your business.
The Value Calculus: Calculating AI ROI
How to prove it's worth it. Learn the formulas for Return on Investment (ROI) and the intangible value of AI transformation.
The Human Element: Change Management for AI
AI is 20% technology and 80% people. Learn how to lead organizations through the cultural shift of AI adoption.
Module 10: Responsible AI Principles
Master fairness, transparency, accountability, and the impact of bias in AI systems.
The Battle Against Bias: Fairness in AI
AI is a mirror of our data. Learn how to identify and mitigate bias to ensure your AI systems are fair and equitable.
Inside the Black Box: Transparency and Explainability
Why did the AI say that? Learn how to peel back the layers of complex models to understand the 'Why' behind every decision.
The Standard of Trust: Accountability and Robustness
Prepare for the unexpected. Learn how to build resilient AI systems that maintain performance under stress and include clear lines of human responsibility.
The Footprint of Intelligence: Social and Environmental Impact
AI at what cost? Learn how to calculate and mitigate the energy consumption and societal impacts of large-scale AI deployment.
Module 11: Security and Privacy in AI Systems
Learn data privacy, access control, and the AWS Shared Responsibility Model for AI.
Locking the Vault: Data Privacy and Encryption
Protecting the oil of AI. Learn how to secure your training data and inference logs using encryption and isolation on AWS.
The Gatekeeper: Identity and Access Management (IAM)
Who gets to talk to the AI? Master the principles of Identity and Access Management to ensure only authorized users and services touch your models.
The Partnership: Shared Responsibility for AI
Who is responsible for what? Master the boundary between AWS and the Customer in the complex world of AI/ML security.
The Battle of Wits: Prompt Injection and Attacks
Master the defense against GenAI hackers. Learn how to protect your Large Language Models from Prompt Injection, Jailbreaking, and Data Leakage.
Module 12: Governance and Compliance
Ensure auditability, traceability, and regulatory compliance in your AI deployments.
The Paper Trail: Auditability and Traceability
Who, what, and when. Learn how to maintain an immutable record of every AI decision for security and legal compliance.
The Watchmen: CloudWatch and CloudTrail
Master the tools of observation. Learn the difference between monitoring performance with CloudWatch and auditing actions with CloudTrail.
The Compliance Shield: AWS Artifact
Passing the audit. Learn how to access AWS's global compliance reports for SOC, HIPAA, and GDPR to prove your AI system is secure.
The Governance Toolkit: Managing the AI Lifecycle
From cradle to grave. Learn how to use SageMaker Model Cards, Registry, and Role Manager to govern your AI system from birth to retirement.
Module 13: AI Project Lifecycle
Step through data collection, model evaluation, and continuous monitoring pipelines.
The Hardest Part: Data Collection and Preparation
Garbage In, Garbage Out. Master the '80% rule' of machine learning and learn how to clean, label, and prepare your data for success.
The Prompt and the Model: Selecting Your AI Brain
Choosing the right engine for the job. Learn how to navigate the Bedrock model catalog and master the art of Prompt Engineering.
The Quality Gate: Evaluating AI Outputs
Is it good? Learn how to measure the accuracy, safety, and helpfulness of your AI models using automated and human evaluation methods.
The Launchpad: Deployment and Monitoring
Going live with confidence. Learn the strategies for deploying AI models at scale and monitoring them for performance and drift.
Module 14: Cost, Performance, and Risk Awareness
Manage AI cost drivers, latency tradeoffs, and scale workloads responsibly.
The Price of Intelligence: AI Cost Drivers
Master the bill. Learn how tokens, instances, and storage define your AWS AI budget and how to optimize for every dollar.
The Performance Scale: Latency vs. Accuracy
Faster or Smarter? Learn how to balance the speed of your AI response with the quality of the intelligence.
The Silent Failure: Infrastructure Risk Awareness
Protecting the uptime. Learn how to mitigate risks like Service Limit exhaustion, Quota limits, and regional outages.
The Growth Engine: Scaling AI Responsibly
From one user to millions. Master the scaling mechanisms in AWS Bedrock and SageMaker to handle explosive growth without breaking the bank.
Module 15: Human and Organizational Impact
Navigate workforce transformation, change management, and building AI literacy.
The AI-First Organization: Building the Strategy
Master the long-game. Learn how to transform a traditional business into an AI-first organization using the feedback loop of data.
The Career Ladder: AI Roles and Paths
Where do you go from here? Explore the different career paths in the AWS AI ecosystem, from Business Practitioner to Machine Learning Engineer.
The Battle Plan: Preparing for the AIF-C01 Exam
Everything you need to cross the finish line. Master the exam structure, timing, and 'Mindset' required to pass the AWS AI Practitioner certification.
The Infinite Learner: Staying Ahead in AI
AI moves fast. Learn the habits and resources you need to maintain your professional edge in a field that evolves every day.
Module 16: Exam Readiness and Review
Final review of key concepts, scenario-based strategies, and exam readiness checklist.
Course Overview
Format
Self-paced reading
Duration
Approx 6-8 hours
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