Overview

The tutorials got you a chatbot. The certification asks for more. The NVIDIA NCP-GENL exam tests whether you understand generative AI at a professional level - not just the concepts, but the engineering: how transformers work, how LLMs are trained and fine-tuned, how inference is optimized, and how NVIDIA's tools fit together from training to production. This book is your preparation guide. What this book covers: Every domain on the exam blueprint - transformer architecture, tokenization, scaling laws, distributed training, fine-tuning (LoRA, QLoRA, P-Tuning, SFT), alignment (RLHF, DPO), quantization (GPTQ, AWQ, FP8), KV-cache optimization, the TensorRT-LLM + Triton stack, NVIDIA NIM, GPU architecture, RAG pipelines, prompt engineering, AI agents, evaluation benchmarks, and responsible AI. How this book works: Every chapter opens with exam objectives and closes with review questions. Callout boxes flag exam tips, NVIDIA tooling, and concepts that matter beyond the test. A full-length mock exam with detailed explanations gives you a realistic dry run. Who this book is for: ML engineers, data scientists, AI developers, and technical professionals preparing for the NCP-GENL certification. Assumes Python proficiency, basic ML knowledge, and some hands-on LLM experience. The tools will change. The principles hold. This book teaches both.

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