NCP-GENL Practice Test — Free NVIDIA Certified Professional: Generative AI LLMs Questions with Explanations

Which NVIDIA library is primarily used for optimizing and deploying deep learning inference models?

Refer to the exhibit. An engineer is fine-tuning an LLM using the provided configuration. What is the primary purpose of applying 'yarn' scaling in this architecture?

Which technique is most effective for mitigating data leakage during the training of an LLM on time-series-related document data?

An LLM inference service on NVIDIA Triton Inference Server is experiencing occasional out-of-memory (OOM) errors on the GPU. The team wants to monitor GPU memory usage to predict and prevent OOM conditions. Which NVIDIA tool provides real-time GPU memory metrics that can be integrated with Prometheus for alerting?

What is the primary function of the 'rank' parameter in LoRA?

A financial institution uses NVIDIA NeMo Guardrails to enforce ethical guidelines in its customer-facing LLM. During testing, the model occasionally generates responses that violate the company's policy against offering investment advice. The guardrails are configured with a set of dialog flows and safety checks. What is the most effective way to address this issue?

Refer to the exhibit. Given this NeMo configuration, which prompt modification would best improve the reliability of technical support queries?

Which hardware architecture feature is specifically leveraged by TensorRT to accelerate FP16 and INT8 matrix multiplications?

Refer to the exhibit. The Triton Inference Server is failing to load a model. The configuration shows two instances assigned to GPU 0. What is the most likely cause and the correct remediation?

When optimizing a Generative AI model using NVIDIA TensorRT-LLM, which component is primarily responsible for managing the KV cache to minimize memory fragmentation?

A team is training a 13B-parameter LLM on 8 NVIDIA A100 GPUs using NVIDIA NeMo. They observe that the all-reduce communication during data-parallel training consumes nearly 40% of each iteration. Which of the following changes is most likely to reduce this communication overhead while preserving convergence?

When fine-tuning a Large Language Model using Low-Rank Adaptation (LoRA), which architectural component is primarily modified to reduce computational overhead while maintaining performance?

When optimizing a model using NVIDIA TensorRT, what is the primary benefit of enabling 'layer fusion' during the optimization process?

A team is deploying a 13B-parameter decoder-only LLM on a single NVIDIA A100 40GB GPU for a real-time chatbot. During load, the process runs out of memory even though the model weights in FP16 require roughly 26GB. The team wants to reduce GPU memory usage with minimal impact on output quality and no change to the model architecture. Which technique is most appropriate?

What is the primary role of a Model Repository in the NVIDIA Triton Inference Server architecture?

When preparing a proprietary technical manual dataset for a RAG pipeline, which data preprocessing step is most critical to ensure the LLM avoids hallucinations regarding specific product configurations?

What is the primary purpose of 'Few-Shot Prompting' in the context of LLM optimization?

A research team wants to train a large decoder-only LLM where each token's representation is computed independently of token order, then inject order information afterward. They are choosing between learned absolute positional embeddings and sinusoidal absolute positional embeddings. Which statement accurately characterizes the tradeoff they face?

Refer to the exhibit. Which prompt engineering technique would best force the model to prioritize technical detail over marketing language?

In the context of model optimization, why is 'graph surgery' sometimes required before building a TensorRT engine?