Ncp-Genl: Generative AI LLMs
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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