RELIABLE NCA-GENM EXAM SIMULATIONS & NCA-GENM LATEST TEST GUIDE

Reliable NCA-GENM Exam Simulations & NCA-GENM Latest Test Guide

Reliable NCA-GENM Exam Simulations & NCA-GENM Latest Test Guide

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Tags: Reliable NCA-GENM Exam Simulations, NCA-GENM Latest Test Guide, NCA-GENM Valid Dumps Free, Latest NCA-GENM Examprep, NCA-GENM Study Test

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NVIDIA Generative AI Multimodal Sample Questions (Q87-Q92):

NEW QUESTION # 87
You are training a multimodal model that combines text and images. You observe that the model is heavily biased towards the text modality and largely ignores the image data. Which of the following strategies could you use to address this modality imbalance? (Select all that apply)

  • A. Oversample the image data during training.
  • B. Increase the learning rate for the image-related parameters of the model.
  • C. Decrease the learning rate for the text-related parameters of the model.
  • D. Use a modality-specific loss weighting scheme, assigning a higher weight to the loss component derived from the image data.
  • E. Reduce the dimensionality of the image features to match the dimensionality of the text embeddings.

Answer: A,B,C,D

Explanation:
Options A, B, C, and D are effective strategies for addressing modality imbalance. Increasing the learning rate for the underutilized modality (image) or decreasing it for the dominant one (text) encourages the model to pay more attention to the image data. Modality-specific loss weighting directly emphasizes the importance of the image modality during training. Oversampling image data ensures that the model sees more image examples and is less likely to be biased towards the text modality. Option E might lead to loss of important information from the image modality.


NEW QUESTION # 88
You are tasked with evaluating the scalability of a multimodal generative model deployed on an NVIDIAAI 00 GPU. The model processes text, images, and audio. Which of the following metrics and tools would be MOST relevant to monitor and analyze?

  • A. GPU utilization, GPU memory usage, and throughput (samples per second).
  • B. CPU utilization and memory usage.
  • C. Network latency and bandwidth.
  • D. CUDA core utilization and Tensor Core utilization.
  • E. Disk 1/0 and storage capacity.

Answer: A,D

Explanation:
GPU utilization, GPU memory usage, and throughput (samples per second) are crucial for assessing GPU workload and processing speed. CUDA and Tensor Core utilizations show how effectively the NVIDIA GPU's parallel processing capabilities are being used. While CPU and network performance can be bottlenecks, the GPU is the primary resource to evaluate for model scalability. Disk 1/0 is relevant for large datasets but less so for real-time inference.


NEW QUESTION # 89
You are deploying a multimodal Generative A1 model on a cloud platform. The model takes video and text as input to generate video descriptions. The model's performance needs to be monitored to ensure it meets certain performance SLAs. Which of the following metrics are MOST crucial to monitor in a production environment to ensure both computational efficiency and output quality? (Select TWO)

  • A. GPU utilization.
  • B. Inference latency (time per request).
  • C. BLEU score (or similar text generation metric) for generated descriptions.
  • D. Model size on disk.
  • E. Number of lines of code in the model.

Answer: B,C

Explanation:
Inference latency directly impacts the user experience and resource utilization. A high latency indicates potential bottlenecks. The BLEU score (or similar metric) measures the quality of the generated text, ensuring that the model is producing accurate and relevant descriptions. GPU utilization is important but secondary compared to latency and quality. Model size and lines of code are not direct indicators of runtime performance or output quality.


NEW QUESTION # 90
You are deploying a text-to-speech application using NVIDIA Riv
a. The application needs to handle a large volume of concurrent requests with minimal latency. Which of the following Riva deployment configurations would be MOST appropriate?

  • A. Deploying Riva on CPU using multiprocessing.
  • B. Deploying Riva on a single CPU core with a small batch size.
  • C. Deploying Riva on a single GPU with a large batch size.
  • D. Deploying Riva on a single GPU using TensorRT for model optimization, without using Triton Inference Server.
  • E. Deploying Riva across multiple GPUs using Triton Inference Server with dynamic batching.

Answer: E

Explanation:
For high-throughput, low-latency applications, deploying Riva across multiple GPUs using Triton Inference Server is optimal. Triton enables dynamic batching, which groups incoming requests to maximize GPU utilization, and allows for scaling across multiple GPUs to handle increased load. Riva leverages gRPC to communicate with Triton.


NEW QUESTION # 91
You are tasked with monitoring a deployed multimodal model that takes text and image inputs to predict customer satisfaction. The model is deployed in a production environment and handles thousands of requests per day. Which of the following monitoring metrics would be MOST crucial for identifying potential issues related to data drift and model degradation?

  • A. GPU utilization of the inference server.
  • B. Average prediction latency.
  • C. Distribution of input text length and image size.
  • D. Distribution of predicted customer satisfaction scores.
  • E. The model's training loss.

Answer: C,D

Explanation:
Monitoring the distribution of input features (B) helps detect data drift, indicating that the input data characteristics have changed compared to the training data. Monitoring the distribution of predicted satisfaction scores (D) can reveal if the model's predictions are becoming less accurate or biased over time. Average prediction latency (A) and GPU utilization (E) are more related to infrastructure performance. Training loss (C) is irrelevant once the model is deployed.


NEW QUESTION # 92
......

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