NCA-GENL Practice Questions: Model Deployment and Inference Domain

Correct Answer: B

Explanation: NVIDIA AI Enterprise provides a comprehensive suite of AI tools that are optimized for NVIDIA hardware, ensuring seamless integration and enhanced performance for deploying AI models.

Correct Answer: A

Explanation: Batching requests is an effective strategy to reduce latency in NVIDIA Triton Inference Server. By processing multiple inputs simultaneously, the server can make better use of GPU resources, reducing the per-request overhead. Increasing the model size or using single-precision floating point can increase latency due to higher computational demands, and disabling model optimization would typically degrade performance.

Correct Answer: C

Explanation: Implementing input sanitization and validation is a crucial strategy to prevent prompt injection attacks, where malicious inputs might manipulate the generative AI's behavior. Chain-of-thought prompting and instruction tuning are techniques to improve model performance, not security measures. Larger context windows are for handling more input data, not for security.

Correct Answer: D

Explanation: NVIDIA AI Enterprise provides a comprehensive suite of tools and frameworks for managing AI workflows in enterprise environments, ensuring compatibility and optimization across NVIDIA's AI ecosystem.

Correct Answer: C

Explanation: A key consideration in NVIDIA's ethical AI framework when deploying generative AI models is implementing bias detection and mitigation strategies to ensure fair and responsible AI outcomes. This involves identifying potential biases in training data and model predictions and taking steps to minimize them. Options A and D focus on technical aspects rather than ethical considerations, and option B, while important, does not directly address bias mitigation.

Correct Answer: B

Explanation: TensorRT-LLM is specifically designed to optimize deep learning models, including large language models, for inference on NVIDIA GPUs. It provides capabilities such as precision calibration, layer fusion, and kernel auto-tuning, which are crucial for deploying models on edge devices with limited resources. NVIDIA NeMo is more focused on model development and training, Triton Inference Server is used for deploying models at scale, and NVIDIA AI Enterprise provides a broader suite of AI tools for enterprise deployment.

Correct Answer: C

Explanation: Triton Inference Server supports multiple frameworks, allowing seamless scaling of applications by integrating models from different sources and optimizing resource usage. It does not directly increase accuracy, enable model parallelism (handled at the model level), or automatically select models.

Correct Answer: B

Explanation: Mixed precision training reduces training time and memory usage by utilizing lower precision (e.g., FP16) for calculations while maintaining model accuracy with selective higher precision (FP32) operations. This leads to faster computation and reduced memory footprint, but it does not inherently increase accuracy, simplify architecture, or eliminate fine-tuning.

Correct Answer: D

Explanation: Content filtering is a direct method to prevent malicious inputs from affecting the model's behavior by identifying and blocking inappropriate or harmful content. Chain-of-thought prompting and instruction tuning are techniques to improve model responses, and prompt templates standardize input but do not inherently prevent injection attacks.

Correct Answer: C

Explanation: LoRA (Low-Rank Adaptation) is used to efficiently fine-tune large language models by adapting only a subset of parameters, reducing the computational burden and memory footprint. This makes it possible to fine-tune models effectively with limited resources. It does not increase vocabulary size, freeze layers, or directly enhance multimodal capabilities.