NVIDIA NCA-GENL (Gen AI & LLMs) Study Guide & Cheat Sheet

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NVIDIA NCA-GENL (Generative AI & LLMs) Study Guide

Questions	50 multiple choice
Time limit	60 minutes
Price	$135 USD
Delivery	Online, remotely proctored
Scoring	Pass/fail
Validity	2 years
Prerequisites	None formal; basic AI/ML familiarity recommended
Language	English

Exam domains

Domain	Weight	What it covers
Core Machine Learning & AI Fundamentals	30%	The largest domain: AI vs ML vs deep learning, neural networks, supervised/unsupervised/self-supervised learning, training vs inference, and the math intuition (loss functions, gradients, backpropagation) behind how models learn. Also covers the transformer architecture and self-attention that underpin every modern LLM. Expect roughly 15 of your 50 questions here.
LLM Fundamentals & Prompt Engineering	25%	How large language models work end to end: tokenization, embeddings, context windows, autoregressive next-token generation, and decoding controls like temperature and top-p. Heavy focus on prompt engineering — zero-shot, few-shot, chain-of-thought, and system prompts — plus retrieval-augmented generation (RAG) to ground responses in external data.
Data Analysis, Preprocessing & Feature Engineering	15%	Preparing data for generative and ML workloads: cleaning, deduplication, normalization, handling missing values, tokenization and chunking of text, and building quality datasets. Touches data exploration, feature engineering, and GPU-accelerated data pipelines such as RAPIDS.
Experimentation & Model Evaluation	15%	Designing experiments and judging model quality: train/validation/test splits, overfitting vs underfitting, and the metrics that matter for LLMs — perplexity, ROUGE, and BLEU — alongside human and LLM-as-judge evaluation. Also covers benchmarking, A/B testing, and guarding against bias and hallucination.
LLM Development, Integration & Deployment	15%	Taking models to production: fine-tuning approaches (SFT, LoRA/PEFT), alignment via RLHF, and the NVIDIA serving stack — NeMo, NIM inference microservices, Triton Inference Server, and TensorRT-LLM. Includes building applications with libraries like LangChain and Hugging Face, plus guardrails and responsible deployment.

Who it’s for: Associate-level practitioners — developers, data scientists, and ML engineers — who build, integrate, and deploy generative-AI and LLM applications using NVIDIA tools such as NeMo, NIM, Triton, and TensorRT-LLM.

Study & test-day tips

Budget your time: 50 questions in 60 minutes is about 70 seconds each. Answer what you know quickly, flag the rest, and circle back so the clock never traps you on one hard item.
The exam is conceptual, not a coding test. Know what a technology is FOR — what problem LoRA, RAG, or a NIM microservice solves — rather than memorizing exact API calls or hyperparameter values.
Master the transformer story end to end: tokens in, embeddings, self-attention, and autoregressive token-by-token generation out. Core ML & AI Fundamentals is 30% of the exam, so this pays off most.
Know the four ways to improve an LLM's output and when to use each: prompt engineering (cheapest), RAG (add fresh/private knowledge), fine-tuning/PEFT (change behavior), and RLHF (align to preferences).
RAG vs fine-tuning is a classic distractor pair. RAG injects external knowledge at inference without retraining; fine-tuning bakes new behavior into the weights. Match the technique to whether the gap is knowledge or behavior.
Memorize the evaluation metrics and what they measure: perplexity for language-model fit, ROUGE for summarization (recall-oriented), BLEU for translation (precision-oriented), plus human and LLM-as-judge for open-ended quality.
Learn the NVIDIA gen-AI stack by job: NeMo (build/train/customize), NIM (deploy as a microservice), Triton (serve at scale), TensorRT-LLM (optimize inference), NeMo Retriever (RAG embeddings), NeMo Guardrails (safety).
Watch for qualifier words like 'most likely', 'primary purpose', and 'best'. Two options are often defensible; the qualifier and the most specific answer pick the winner.
Don't leave blanks — there's no penalty for guessing. Eliminate two options and choose the more specific remaining answer.
Take the timed mock in this app at least twice and book only when you're consistently scoring 80%+. The exam is pass/fail, so aim for margin, not a coin flip.

Cheat sheet

How an LLM works

Tokenization: text is split into tokens (subword units), each mapped to an integer ID the model can process
Embeddings: tokens become dense vectors that capture meaning; similar concepts sit close together in vector space
Self-attention: each token weighs the relevance of every other token in the context — the transformer's core mechanism
Autoregressive generation: the model predicts the next token one at a time, feeding each output back as input
Context window: the maximum number of tokens (prompt + output) the model can attend to at once

Decoding & generation controls

Temperature: higher = more random/creative output, lower = more deterministic and focused
Top-p (nucleus sampling): sample from the smallest set of tokens whose probabilities sum to p
Top-k: restrict sampling to the k most probable next tokens
Max tokens: caps the length of the generated response
Greedy decoding: always pick the single most probable next token (deterministic, can be repetitive)

Prompt engineering

Zero-shot: ask the task directly with no examples
Few-shot: include a handful of input/output examples to steer format and behavior
Chain-of-thought: prompt the model to reason step by step, improving multi-step and math problems
System prompt: sets persona, rules, and constraints that apply across the conversation
RAG: retrieve relevant external documents and add them to the prompt to ground answers and reduce hallucination

Customizing & aligning models

SFT (supervised fine-tuning): further train on labeled task examples to specialize behavior
LoRA / PEFT: parameter-efficient fine-tuning — train small added weights instead of the full model, saving compute and memory
RLHF: reinforcement learning from human feedback aligns outputs to human preferences
Alignment: making a model helpful, honest, and harmless via instruction tuning, RLHF, and guardrails
RAG vs fine-tuning: RAG adds knowledge at inference; fine-tuning changes the model's learned behavior

Evaluation metrics

Perplexity: how well a language model predicts a sample — lower is better
ROUGE: overlap-based, recall-oriented metric for summarization quality
BLEU: n-gram precision metric originally for machine translation
Human evaluation & LLM-as-judge: rate open-ended quality where automatic metrics fall short
Overfitting vs underfitting: too tailored to training data vs too simple to capture the pattern

NVIDIA gen-AI stack

NeMo: framework to build, train, and customize LLMs and other generative models
NIM (NVIDIA Inference Microservices): packaged, optimized model endpoints for easy deployment
Triton Inference Server: serves models from any framework at scale with dynamic batching
TensorRT-LLM: optimizes LLM inference (quantization, fused kernels) for low latency and high throughput
NeMo Retriever & NeMo Guardrails: embedding/retrieval for RAG, and programmable safety/topic controls
Foundations: CUDA, NGC catalog, RAPIDS for data, plus PyTorch, Hugging Face, and LangChain for development

Glossary

Alignment: The process of making a model's behavior helpful, honest, and harmless and consistent with human intent, typically via instruction tuning, RLHF, and guardrails.
Attention: A mechanism that lets a model weigh the relevance of different tokens when producing each output; self-attention is the core of the transformer.
Autoregressive: Generating output one token at a time, where each new token is conditioned on all the tokens produced so far.
BLEU: A precision-based, n-gram overlap metric originally designed to evaluate machine translation quality against reference text.
Chain-of-thought: A prompting technique that asks the model to reason step by step, improving performance on multi-step and reasoning tasks.
Context window: The maximum number of tokens (prompt plus generated output) a model can consider at once.
CUDA: NVIDIA's parallel computing platform and programming model that lets software run general-purpose computation on GPUs.
Embedding: A dense numeric vector representing a token, word, or document so that semantic similarity corresponds to closeness in vector space.
Few-shot prompting: Providing a small number of input/output examples in the prompt to guide the model's format and behavior.
Fine-tuning: Further training a pretrained model on task- or domain-specific data to adapt its behavior.
Hallucination: When a model produces fluent but factually incorrect or fabricated content; RAG and grounding help reduce it.
Hugging Face: A popular ecosystem and library hub for sharing, loading, and running pretrained transformer models and datasets.
Inference: Running a trained model on new inputs to produce outputs; for LLMs, generating text token by token.
LangChain: An open-source framework for building LLM applications by chaining prompts, models, tools, memory, and retrieval.
LoRA: Low-Rank Adaptation — a parameter-efficient fine-tuning method that trains small added weight matrices instead of the full model.
NeMo: NVIDIA's framework for building, training, and customizing large language models and other generative AI models.
NeMo Guardrails: An NVIDIA toolkit for adding programmable safety, topic, and behavior controls to LLM applications.
NeMo Retriever: NVIDIA's set of microservices for embedding and retrieval that power retrieval-augmented generation pipelines.
NGC: NVIDIA's catalog of GPU-optimized containers, pretrained models, and SDKs.
NIM: NVIDIA Inference Microservices — packaged, GPU-optimized model endpoints that make deploying models as APIs fast and consistent.
PEFT: Parameter-Efficient Fine-Tuning — a family of methods (including LoRA) that adapt models by updating a small fraction of parameters.
Perplexity: A metric for how well a language model predicts a sample of text; lower perplexity means better predictions.
Prompt engineering: Crafting and structuring inputs — instructions, examples, and context — to get better outputs from an LLM without changing its weights.
PyTorch: A widely used open-source deep learning framework for building and training neural networks, including transformers.
Quantization: Reducing the numeric precision of model weights (e.g., to 8- or 4-bit) to shrink memory use and speed up inference.
RAG: Retrieval-Augmented Generation — retrieving relevant external documents and adding them to the prompt so the model grounds answers in current or private data.
RAPIDS: NVIDIA's suite of GPU-accelerated libraries for data science and data preprocessing, mirroring pandas and scikit-learn APIs.
RLHF: Reinforcement Learning from Human Feedback — aligning a model's outputs to human preferences using a learned reward signal.
ROUGE: A recall-oriented, overlap-based metric commonly used to evaluate text summarization quality against reference summaries.
SFT: Supervised Fine-Tuning — training a model on labeled input/output examples to specialize its behavior for a task.
TensorRT-LLM: An NVIDIA library that optimizes large language model inference (fused kernels, quantization) for low latency and high throughput.
Tokenization: Splitting text into tokens (often subword units) and mapping them to integer IDs that a model can process.
Transformer: The neural network architecture, based on self-attention, that underpins modern LLMs and most generative AI models.
Triton Inference Server: NVIDIA's open-source server for deploying models from any framework at scale, with features like dynamic batching.

HOW TO // AI is not affiliated with or endorsed by NVIDIA. NCA-GENL is a certification of NVIDIA Corporation; we reference it descriptively. All questions are original.