NVIDIA Certified Associate: Generative AI LLMs (NCA-GENL) Practice Questions: Ethical AI and Responsible Development Domain

Correct Answer: B

Explanation: NVIDIA NeMo offers pre-trained models and a suite of tools that facilitate efficient fine-tuning of large language models, especially on NVIDIA DGX systems which are optimized for high-performance computing. This combination allows for rapid development and deployment of AI models. While NeMo helps in model training and fine-tuning, it does not inherently address bias (A), reduce data requirements (C), or automate deployment to edge devices (D).

Correct Answer: B

Explanation: Low-Rank Adaptation (LoRA) is an efficient fine-tuning method that reduces the number of trainable parameters by adapting only a small subset of the model's weights. This approach is computationally less expensive and helps preserve accuracy. Full precision training (option A) is costly, increasing the learning rate (option C) can lead to instability, and a smaller dataset (option D) may not capture the necessary variability.

Correct Answer: D

Explanation: NVIDIA AI Enterprise provides comprehensive frameworks for ethical AI development, which include tools to filter and manage outputs for bias and safety. While prompt engineering (A) can guide outputs, it does not inherently reduce bias. Data augmentation (B) helps with training diversity but isn't a direct bias mitigation strategy. TensorRT-LLM (C) focuses on optimization and does not address ethical concerns.

Correct Answer: B

Explanation: Dynamic batching strategies in NVIDIA Triton Inference Server help manage and reduce latency by efficiently grouping incoming requests, thus optimizing resource utilization. Using only CPU resources (A) would typically increase latency. Reducing vocabulary size (C) and training on synthetic data (D) are not direct methods for managing latency during deployment.

Correct Answer: A

Explanation: Increasing the batch size is a common strategy to improve throughput when using NVIDIA TensorRT-LLM. Larger batch sizes allow more data to be processed simultaneously, maximizing hardware utilization. Reducing layers or changing architectures affects the model's structure, while more training data is relevant to model training, not inference throughput.

Correct Answer: B

Explanation: NVIDIA NeMo's RLHF (B) capabilities allow developers to align model outputs with human values, ensuring responsible AI development by adjusting the model's behavior based on feedback. Pre-built models (A) do not inherently ensure responsibility. Prompt injection prevention (C) helps with security but not ethical development. Chain-of-thought prompting (D) aids reasoning but is not directly related to ethical alignment.

Correct Answer: C

Explanation: LoRA (Low-Rank Adaptation) is a technique that allows efficient fine-tuning of large language models by reducing the number of trainable parameters. This results in reduced computational resource requirements, making it suitable for environments with limited resources. It does not necessarily reduce model size or improve generalization capabilities directly, nor is it primarily focused on data sample efficiency.

Correct Answer: B

Explanation: NVIDIA AI Enterprise provides comprehensive support and integration with existing enterprise IT systems, making it easier for businesses to deploy and manage AI models. While it offers access to a suite of tools, it does not include exclusive hardware or free access to all software. Automatic model training and deployment are not features of NVIDIA AI Enterprise.

Correct Answer: C

Explanation: Content filtering in NVIDIA's responsible AI framework is used to prevent the generation of inappropriate or harmful content. It ensures that AI applications adhere to ethical guidelines and do not produce outputs that could be offensive or damaging. Enhancing creativity, aligning with user expectations, and optimizing performance are not the primary goals of content filtering.

Correct Answer: B

Explanation: Implementing bias detection and mitigation strategies is crucial for ethical AI deployment, as it ensures that the model's outputs are fair and unbiased. NVIDIA AI Enterprise provides tools and frameworks to help detect and mitigate biases in AI models. Option A could lead to harmful outputs, option C ignores ethical considerations, and option D poses risks of misuse.