NVIDIA GenAI LLM study guide
NVIDIA GenAI LLM · Work through your exam, one topic at a time.
The NVIDIA-Certified Associate Generative AI LLM credential, exam code NCA-GENL, is an entry-level, Associate-level certification that validates foundational concepts for developing, integrating, and maintaining AI-driven applications with generative AI and large language models on NVIDIA solutions. NVIDIA describes the target candidate as an associate developer who contributes to the development, programming, and quality assurance of GenAI/LLM systems: building datasets, selecting and training models, running model testing and debugging, and understanding model deployment. The NVIDIA GenAI LLM study guide has 5 sections and about 1,630 words.
01Core Machine Learning and AI Knowledge
A decoder-only transformer maps token IDs to embeddings, injects positional information (RoPE, ALiBi, or learned tables), and passes them through N blocks of causally masked self-attention plus a feed-forward network. In attention, queries, keys, and values are linear projections; scores QK^T are scaled by 1/sqrt(d head) before masking and softmax.
337 words, about 2 min02Data Analysis
LLM data work is a pipeline: collect, extract text, filter, deduplicate, decontaminate, and mix. Quality filters are usually cheap heuristics first - document length, symbol-to-word ratios, stop-word presence, repeated n-gram fraction, language ID confidence - then model-based filters such as perplexity scoring or classifier scores from a small labeled set.
313 words, about 2 min03Experimentation
Good experiments start with a hypothesis, an explicit baseline, and controlled variables. Ablations change one factor at a time (for example, data mixture, LoRA rank, learning rate, or decoding settings) so effects are attributable.
340 words, about 2 min04Software Development
Candidates should read a training or inference stack end to end. On the training side: CUDA kernels, NCCL for collectives, mixed precision (BF16 and FP8 with scaling), gradient accumulation, activation checkpointing, and distributed strategies - data parallel, tensor parallel (Megatron-style, head-dim divisibility required, best inside NVLink nodes), pipeline parallel (interleaved schedules reduce the bubble but add memory pressure), sequence/context parallel for long context, and ZeRO stages/FSDP for sharding optimizer state, gradients, and parameters.
307 words, about 2 min05Trustworthy AI
Trustworthy AI for LLMs spans alignment, safety, privacy, fairness, and transparency. Alignment covers instruction tuning, RLHF/DPO, and the helpfulness-harmlessness tradeoff; over-tuned safety produces over-refusal, which is measured as a false-positive rate on benign prompts, not by eyeballing a few outputs.
333 words, about 2 min