NVIDIA-Certified Associate: Generative AI LLMs

Are you ready to become NVIDIA-Certified in Generative AI LLMs?
The NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) certification validates your ability to build, deploy, and optimize Large Language Models using NVIDIA’s GPU-accelerated ecosystem. Passing this exam proves you understand LLM architecture, prompt engineering, RAG, fine-tuning, and responsible AI—skills that are in explosive demand.

But the exam is challenging. It tests not just theory, but applied knowledge of NVIDIA NeMo, TensorRT-LLM, Triton Inference Server, and real-world LLM deployment constraints. You cannot pass by memorizing concepts alone. You need exam-level practice.

This course gives you exactly that.

What You Get – 6 Full-Length Practice Tests

This resource contains 6 complete practice tests with over 600 unique, high-fidelity questions, crafted to mirror the official NCA-GENL exam in difficulty, style, and domain weighting.

This practice test suite primarily teaches you how to think like an NVIDIA GenAI associate by testing and reinforcing knowledge across all exam domains:

1. Foundations of Generative AI & LLMs

   • Transformer architecture (attention, positional encoding, feed-forward networks)

   • Pre-training objectives (causal LM, masked LM)

   • Scaling laws, inference vs. training compute

2. LLM Architecture & Optimization (NVIDIA Focus)

   • Quantization (INT8, FP8, INT4) – when and why

   • TensorRT-LLM optimizations (in-flight batching, KV caching)

   • Model parallelism (tensor, pipeline, sequence)

3. Prompt Engineering & In-Context Learning

   • Zero-shot, few-shot, chain-of-thought, self-consistency

   • System prompts, stop tokens, logit biases

   • Handling hallucinations and recency bias

4. Retrieval-Augmented Generation (RAG)

   • Chunking strategies, embedding models, vector databases (FAISS, RAFT)

   • RAG vs. fine-tuning tradeoffs

   • NVIDIA NeMo Retriever microservices

5. Fine-Tuning & Parameter-Efficient Methods

   • Full fine-tuning vs. LoRA, P-tuning, Adapters

   • When to use PEFT vs. full fine-tuning

   • Overfitting, catastrophic forgetting, and data curation

6. Deployment & Inference on NVIDIA GPUs

   • Triton Inference Server (dynamic batching, concurrent model execution)

   • Throughput, latency, memory footprint tradeoffs

   • Multi-GPU and multi-node inference

7. Responsible AI & Security

   • Toxicity filtering, bias detection, prompt injection

   • Model cards, red teaming, data privacy

   • NVIDIA NeMo Guardrails