NCA-GENL Practice Questions: Generative AI Fundamentals Domain

Correct Answer: D

Explanation: Dynamic batching in NVIDIA Triton Inference Server allows multiple requests to be processed together, optimizing GPU utilization and reducing latency. Increasing batch size excessively can lead to higher latency due to queuing. Model ensembles are used for combining models, not directly for latency optimization. Reducing concurrent instances may decrease throughput.

Correct Answer: B

Explanation: NVIDIA Triton Inference Server is designed to optimize inference performance through features such as dynamic batching and concurrent model execution, which are critical for reducing latency and maximizing throughput. Option A is incorrect as support for multiple programming languages, while useful, does not directly impact latency optimization. Option C is incorrect since integration with cloud storage is more related to data management than latency. Option D is incorrect because model version control is important for deployment management but not specifically for latency optimization.

Correct Answer: B

Explanation: Chain-of-Thought prompting improves model performance by encouraging the model to generate intermediate reasoning steps, which can lead to more accurate and coherent outputs. It does not reduce parameters, simplify tokenization, or increase the context window size.

Correct Answer: C

Explanation: A vector database is used in Retrieval-Augmented Generation (RAG) to facilitate the fast retrieval of semantically similar documents by storing embeddings of documents. This allows the generative model to access relevant information quickly, enhancing the quality and relevance of the generated content.

Correct Answer: B

Explanation: LoRA (Low-Rank Adaptation) is used to efficiently fine-tune large language models by introducing low-rank matrices that require fewer parameters, making the process more resource-efficient without significantly altering the original model. Option A is incorrect as LoRA is not specifically for reducing model size for edge deployment. Option C, while a potential outcome, is not the primary purpose of LoRA; it is focused on parameter efficiency. Option D is incorrect as LoRA is not related to the pre-training phase.

Correct Answer: C

Explanation: RAG enhances factual accuracy by retrieving relevant information from external sources, which the model can use to generate more accurate and contextually relevant responses. It does not inherently increase model size, reduce the need for pre-training, or simplify the architecture.

Correct Answer: B

Explanation: NVIDIA TensorRT-LLM is specifically designed to optimize LLM inference by converting models into a format that can efficiently utilize NVIDIA Tensor Cores. This results in improved performance and reduced latency during inference. Option A, NeMo, is primarily for training and model development. Option C, Triton Inference Server, is for serving models but does not perform the model optimization itself. Option D, NVIDIA AI Enterprise, is a suite of AI tools but does not specifically perform model optimization.

Correct Answer: D

Explanation: NVIDIA AI Guardrails is specifically designed to ensure AI systems adhere to ethical guidelines by implementing content filtering, bias mitigation, and other safety measures. While NeMo (A) is used for model training and development, Triton Inference Server (B) for serving models, and AI Enterprise (C) for enterprise deployment, AI Guardrails (D) focuses on maintaining ethical standards.

Correct Answer: B

Explanation: Using diverse and representative datasets is a fundamental approach to mitigating bias in LLMs. This ensures that the model learns from a wide range of perspectives and reduces the likelihood of biased outputs. Option A is incorrect because access controls do not address the underlying bias in model predictions. Option C is incorrect as reducing model size does not inherently reduce bias. Option D is incorrect because limiting vocabulary size can actually hinder the model's ability to produce nuanced and accurate outputs.

Correct Answer: B

Explanation: The Multi-Head Attention mechanism in the Transformer architecture allows the model to focus on different parts of the input sequence simultaneously, capturing long-range dependencies effectively. While positional encoding helps in maintaining the order of tokens, it is the attention mechanism that enables the model to weigh the importance of different tokens.