Fundamentals of AI and ML
Core AI and ML concepts and terminology, practical use cases, and the AI/ML development lifecycle.
- Explain basic AI concepts and terminologies
- Identify practical use cases for AI
- Describe the AI/ML development lifecycle
Written against the official exam guides, domain by domain — every lesson maps to a task statement the exam actually tests. Read the whole library free, without an account.
Understand AI, GenAI, and foundation models on AWS — and pass AIF-C01.
The cloud fundamentals every role needs — your first AWS certification.
Write, deploy, and debug cloud-native apps the way AWS expects — and pass DVA-C02.
Design resilient, secure, and cost-effective AWS architectures — the industry’s most sought-after cloud certification.
Administer identity, storage, compute, and networking on Azure — and pass AZ-104.
Learn cloud concepts and core Azure services from zero — and pass AZ-900.
Monitor, operate, and automate production AWS environments — the operations certification (formerly SysOps Administrator).
Build, operate, and secure data pipelines on AWS — and pass DEA-C01.
Build, deploy and run ML and generative AI on AWS — and pass MLA-C02.
Detect, respond, and lock down AWS workloads at scale — and pass SCS-C03.
Prove hands-on security skills — and pass CompTIA Security+ SY0-801 (V8).
Learn the foundations of IT — hardware, software, networking, data and security — and pass CompTIA Tech+.
Build reliable pipelines on the Databricks Data Intelligence Platform — and pass the Data Engineer Associate exam.
Build, deploy and monitor RAG apps and agents on Databricks — and pass the Generative AI Engineer Associate exam.
Automate, secure and scale CI/CD with GitHub Actions — and pass GH-200.
Learn Git, GitHub collaboration and the modern GitHub toolset — and pass GH-900.
Deploy, operate, and secure workloads on Google Cloud — and pass Associate Cloud Engineer.
Lead generative AI adoption on Google Cloud — and pass the Generative AI Leader exam.
Master Terraform's workflow, configuration, modules, and state — and pass Terraform Associate 004.
Secure cloud data, platforms and applications — and pass ISC2 CCSP (2026 outline).
Start your cybersecurity career — and pass the ISC2 CC exam (2026 outline).
Learn how software testing really works — and pass ISTQB CTFL v4.0.
Learn Kubernetes and the cloud-native ecosystem — and pass the KCNA exam.
Learn Microsoft 365, Copilot and agent administration — security, Purview governance and day-to-day admin — and pass AB-900.
Build, ground and operate AI apps and agents on Microsoft Foundry — and pass AI-103.
Learn AI concepts and build your first apps and agents in Microsoft Foundry — and pass AI-901.
Secure Azure, hybrid and AI workloads end to end — and pass SC-500.
Build, secure, and optimize analytics solutions in Microsoft Fabric — and pass DP-700.
Read Java code like the compiler does — and pass OCP Java SE 25 (1Z0-831).
Lead projects across predictive, agile and hybrid delivery — and pass the new PMP exam.
Revision is re-reading. Every lesson has the same four parts in the same order, so your second pass through a domain takes a fraction of the time your first one did.
Every lesson opens by telling you what you should be able to do by the end of it — so you know before you read whether you can skip it.
Each section answers its own heading in the first sentence and has its own link. You can send someone straight to the part that matters, and so can a search engine.
The exam-relevant angle, stated plainly: not just how the service works, but the trade-off the question will actually hinge on.
The facts worth carrying into the exam room, at the end of every lesson — and collected across the whole library into a per-certification cheat sheet.
This is the actual blueprint for the AWS Certified AI Practitioner, and the 14 lessons that cover it. Every certification in the catalog is mapped the same way — domain, weight, topics, lessons.
Core AI and ML concepts and terminology, practical use cases, and the AI/ML development lifecycle.
Generative AI concepts, its capabilities and limitations for business problems, and the AWS infrastructure for building GenAI applications.
Design considerations for FM-based applications, prompt engineering, training and fine-tuning, and evaluating FM performance — the heaviest-weighted domain.
Responsible development of AI systems, and the importance of transparent and explainable models.
Securing AI systems on AWS, and the governance and compliance regulations that apply to them.
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