NCA-GENLPractice Exam & Study Guide
50
Exam Questions
60
Minutes
70%
Passing Score
135+
Practice Questions
The NVIDIA Certified Associate: Generative AI LLMs exam validates a candidate's fundamental understanding of Large Language Models (LLMs) and the end-to-end pipeline required to deploy them. It tests the ability to differentiate between various model architectures, apply prompt engineering techniques, implement Retrieval Augmented Generation (RAG), and understand the trade-offs between fine-tuning and inference optimization. This certification is designed for AI developers, data scientists, and IT professionals who want to demonstrate their proficiency in utilizing NVIDIA's ecosystem to build generative AI applications. Prerequisites include a basic understanding of Python programming and fundamental machine learning concepts, though no specific prior NVIDIA certification is required.
12 questions to assess your readiness. Get a personalized study plan in 5 minutes.
Start Free DiagnosticNo credit card required
33 practice questions available
25 practice questions available
25 practice questions available
22 practice questions available
30 practice questions available
Master the difference between Encoder-only, Decoder-only, and Encoder-Decoder architectures.
Practice Zero-shot, Few-shot, and Chain-of-Thought prompting techniques.
Study the RAG workflow: document chunking, embedding generation, vector database storage, and retrieval.
Understand the difference between Full Fine-Tuning and Parameter-Efficient Fine-Tuning (PEFT) like LoRA and QLoRA.
Learn about quantization methods (INT8, FP8, 4-bit) and their impact on model performance and memory.
Explore the NVIDIA NIM (NVIDIA Inference Microservices) architecture and deployment benefits.
Understand the role of TensorRT-LLM in optimizing inference throughput and latency.
Review common evaluation metrics for LLMs, such as perplexity and BLEU/ROUGE scores.
Study the concept of 'Temperature' and 'Top-P' sampling in the context of token generation.
Practice identifying the right-sizing of GPU memory for different model parameter counts.
Carefully read the prompt engineering questions to identify the specific technique being requested.
Manage your time strictly; allocate more time to the LLM Fundamentals and RAG sections as they carry high weight.
Eliminate obviously incorrect answers first when dealing with architectural comparisons.
Pay close attention to keywords like 'most efficient' or 'lowest latency' when choosing deployment strategies.
Ensure a stable internet connection if taking the exam remotely via the NVIDIA portal.
Double-check your answers for quantization-related questions, as the math/logic can be tricky.
135+ practice questions, 3 full mock exams, AI-powered study plan.