NVIDIA-Certified Associate: Generative AI LLMs Skills Measured

Understand transformers, attention, embeddings, tokenization, neural network basics, encoder-style models, decoder-style generation, context windows, and autoregressive next-token prediction. Candidates often confuse context with training data: context is supplied at inference time, while training changes model parameters.

Prompting skills include system prompts, task framing, role instructions, zero-shot prompting, few-shot examples, output-format constraints, and prompt evaluation. Alignment and safety concepts matter because a model that follows instructions can still produce unsafe, irrelevant, or ungrounded content if the application lacks policy controls and evaluation.

Software development skills include calling model APIs, handling structured outputs, managing prompts in code, validating responses, building retrieval workflows, testing failure cases, and integrating model outputs into user-facing applications. NVIDIA’s topic list includes Python libraries for LLMs, so candidates should understand how LLM workflows are assembled in code.

RAG connects private or changing knowledge to a model without retraining it. Study ingestion, chunking, metadata, embeddings, vector search, semantic retrieval, reranking, prompt assembly, citations, and answer generation. RAG is useful when the model needs current or domain-specific information, but retrieval quality and grounding must be evaluated separately from generation quality.

Experimentation skills include choosing datasets, selecting benchmarks, comparing prompt variants, measuring retrieval quality, reviewing human judgments, and preventing regressions. A common mistake is treating one good demo answer as proof of system quality. Production candidates should think in terms of repeatable tests, representative examples, failure analysis, and drift monitoring.

Deployment skills include model serving, inference latency, throughput, batching, model versions, GPU utilization, scaling, and rollback strategy. NVIDIA NIM, Triton Inference Server, and TensorRT-LLM relate to serving and optimization concepts, but candidates should understand why inference systems need predictable performance and monitoring rather than only naming tools.

Trustworthy AI includes safety, transparency, accountability, privacy, bias awareness, misuse prevention, and governance records. Guardrails can constrain conversational flow, restrict unsafe content, route tool calls, or enforce response policies. They are not a substitute for evaluation, access control, logging, or human review where risk is high.