NVIDIA DLI Exam Prep — Generative AI with Diffusion Models & LLMs

Comprehensive study roadmap for NVIDIA DLI Generative AI courses — from Diffusion Models, RAG Agents, Agentic AI to LLM Evaluation & Fine-tuning. 10 in-depth lessons with hands-on code, coding assessment practice, and sample questions closely matching the real exam.

Introduction

The NVIDIA DLI Generative AI Exam Prep course helps you systematically review the entire Generative AI/LLM track from NVIDIA Deep Learning Institute — from Diffusion Models, RAG Agents, Agentic AI to LLM Evaluation & Fine-tuning.

How it differs from traditional certifications

NVIDIA DLI is not a MCQ exam — you must write real code on a Jupyter Notebook with cloud GPU. This is why this series focuses on hands-on coding and debug exercises instead of theoretical questions.

Who should take this course?

ML Engineers seeking deep technical certification in Generative AI
Developers who know Python and want to master PyTorch + LLM stack
Candidates preparing for DLI courses: S-FX-14 (Diffusion), S-FX-15 (RAG), C-FX-25 (Agentic), S-FX-34 (Eval/Fine-tune)

Part 1: Deep Learning Foundations

Lesson 1: PyTorch & Neural Network Fundamentals90 min Lesson 2: Transformer Architecture & Attention Mechanism90 min

Part 2: Generative AI with Diffusion Models

Lesson 3: U-Net Architecture & Denoising Basics90 min Lesson 4: DDPM — Forward & Reverse Diffusion90 min Lesson 5: CLIP & Text-to-Image Pipeline90 min

Part 3: LLM Applications & RAG

Lesson 6: LLM Inference Pipeline Design75 min Lesson 7: RAG — Retrieval-Augmented Generation90 min Lesson 8: RAG Agent — Build & Evaluate90 min

Part 4: Agentic AI & LLM Customization

Lesson 9: Agentic AI & Multi-Agent Systems90 min Lesson 10: LLM Evaluation & LoRA Fine-tuning90 min