Mastering Agentic AI Systems — Supplementals

Companion material for Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam. It was written primarily to support the NVIDIA NCP-AAI (NVIDIA Certified Professional — Agentic AI) certification, and its breadth also makes it useful preparation for AWS AIP-C01, Databricks Generative AI Engineer Associate, Google Cloud Professional Machine Learning Engineer, and Microsoft AI-102. See cert_mapping/README.md for exactly which chapters map to which certification's knowledge items.

If you're studying for one of those exams, teaching a course that covers agentic AI systems, or building your own curriculum on top of this material, this repository is for you.

Repository contents

The book itself is organized into 10 Parts and roughly 94 chapters (see cert_mapping/README.md for the authoritative chapter list). Everything else in this repository is keyed to that chapter numbering.

Folder	What's in it
Study_Plan.md	The full chapter text — the primary reading material.
Prerequisite_Knowledge.md	What to know before starting, and where to fill gaps.
complexityCategories.md	How topics are classified by difficulty across the book.
quizzes_ver20JUN26.md	Per-chapter quiz links (external, Google Forms).
simulated_tests_ver20JUN26.md	Full-length practice exam links (external, Google Forms).
cert_mapping/	Chapter-to-certification knowledge-item mappings (H/M/L/N relevance ratings), one CSV per certification, plus source exam guides.
labs/	Hands-on exercises. This folder is being rebuilt from scratch — see the note below.
slides/	Presentation decks, one or more per chapter, PDF.
videos/	Curated third-party YouTube resources, one file per Part. These are references, not contributed material.
code_examples/	Standalone illustrative code snippets pulled from the chapter text.
more_examples/	Additional worked examples.
figures/	Diagrams and figures used in the book, one folder per chapter.
References/	Source material and vendored third-party reference documentation.
ai_tutor/	Resources for using an AI assistant as a study tutor, with example prompts.

About labs/

The 17 files under labs/archive/ are prose excerpts from the chapter text that happen to contain worked code — not finished, standalone exercises, and moved out of the main labs/ listing on purpose so browsing labs/ shows real labs and the template first. A real lab for every chapter is still to be written. If you're looking for hands-on practice today, start with labs/LAB_TEMPLATE.md and the reference lab it links to, which define what a finished lab looks like and are the standard new labs are built against.

How to use this material to study

1. Start with Prerequisite_Knowledge.md to check what background you need.
2. Work through Study_Plan.md chapter by chapter, using cert_mapping/README.md to prioritize chapters most relevant to your target certification.
3. Use the per-chapter quiz links in quizzes_ver20JUN26.md to check retention as you go, and the full-length practice exams in simulated_tests_ver20JUN26.md closer to exam day.
4. Reinforce concepts hands-on with labs/ (see the note above — coverage is still being built out) and by reading the illustrative snippets in code_examples/.
5. Use slides/ for a condensed pass and videos/ for third-party explanations of the same concepts from a different angle.

Licensing

As a work of the United States government, this project is in the public domain within the United States. We also waive copyright and related rights in the work worldwide through the CC0 1.0 Universal public domain dedication. All contributions to this project — prose, code, and otherwise — are released under that same dedication. Third-party video links in videos/ are references and are not themselves contributed material covered by this dedication. See LICENSE.md for the full text.

Contributors

Recognized via the All Contributors specification, which credits documentation, tutorial, video, content, review, ideas, and maintenance work by name — not only code. Credit accrues as you contribute more; it isn't recomputed or reset. The bot integration that auto-updates this table from a PR comment (@all-contributors please add @user for content) needs a one-time GitHub App installation by a repository admin — until that's done, entries are added by editing .all-contributorsrc and running npx all-contributors generate as part of the PR.

Cybonto 🖋 💻 📖 🚧 📆

We're looking for help

The biggest gap here is labs/: every chapter needs a real, hands-on lab written from scratch — the 17 files under labs/archive/ are prose excerpts, not finished exercises, and none of it counts as coverage. That's the focus of this call, but it's not the only track. We're also looking for instructional-content reviewers (chapter text, slides, and quizzes — no coding required), help extending cert_mapping/ to cover more certifications (one, Google's "Generative AI Leader," already has its exam guide sitting here with zero mapping done), and help verifying and growing the videos/ library, where a lot of entries in the later chapters look populated but don't actually link anywhere.

None of this is urgent-crisis territory — it's the normal state of a project one person built fast. See our call for collaborators for the real numbers, what each track involves, and what you get out of it, and CONTRIBUTING.md for how to get started.