NEW NCA-GENM TOP QUESTIONS 100% PASS | EFFICIENT NCA-GENM: NVIDIA GENERATIVE AI MULTIMODAL 100% PASS

New NCA-GENM Top Questions 100% Pass | Efficient NCA-GENM: NVIDIA Generative AI Multimodal 100% Pass

New NCA-GENM Top Questions 100% Pass | Efficient NCA-GENM: NVIDIA Generative AI Multimodal 100% Pass

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NVIDIA Generative AI Multimodal Sample Questions (Q162-Q167):

NEW QUESTION # 162
When deploying a large multimodal model to a resource-constrained environment (e.g., an edge device), which optimization techniques are MOST crucial to consider? (Select all that apply)

  • A. Knowledge distillation to transfer knowledge from a larger, more accurate model to a smaller, faster model.
  • B. Adding more layers to the model to improve accuracy.
  • C. Model quantization to reduce the model's memory footprint and computational requirements.
  • D. Increasing the batch size to improve throughput.
  • E. Pruning to remove less important connections from the model.

Answer: A,C,E

Explanation:
Model quantization, knowledge distillation, and pruning are all effective techniques for reducing the size and computational cost of a model, making it suitable for deployment in resource-constrained environments. Increasing the batch size would typically increase the memory usage. Adding layers would only increase the size.


NEW QUESTION # 163
You are deploying a multimodal model that uses both video and audio data for real-time emotion recognition. The model is deployed on an edge device with limited computational resources. Which optimization techniques would be MOST effective for reducing latency and improving the model's inference speed on the edge device?

  • A. Transmitting the video and audio data to a cloud server for inference.
  • B. Increasing the resolution of the video input.
  • C. Using full precision (FP32) for all model operations.
  • D. Quantizing the model to a lower precision (e.g., INT8) and pruning less important connections.
  • E. Increasing the model's complexity to improve accuracy.

Answer: D

Explanation:
Quantization to a lower precision (e.g., INT8) significantly reduces the model size and computational requirements, leading to faster inference speeds on edge devices. Pruning further reduces the model's complexity. Increasing model complexity (A) or using FP32 (B) would increase latency. Offloading to the cloud (D) introduces network latency. Increasing video resolution (E) increases the computational load.


NEW QUESTION # 164
You're training a multimodal model on text, image, and audio dat
a. During training, you encounter 'CUDA out of memory' errors. Your dataset is large, and you have a GPU with limited memory. Which of the following strategies would be MOST effective to mitigate this issue without significantly reducing model performance?

  • A. Decrease the number of layers in the model.
  • B. Implement gradient accumulation.
  • C. Increase the resolution of the input images.
  • D. Reduce the batch size.
  • E. Use mixed-precision training (e.g., FP16 or BFI 6).

Answer: B,D,E

Explanation:
Reducing the batch size (A) directly decreases memory consumption. Mixed-precision training (B) reduces the memory footprint of the model's weights and activations. Gradient accumulation (D) allows for a larger effective batch size without increasing memory usage per iteration. Decreasing the number of layers (C) can reduce memory usage, but it might also significantly reduce model performance. Increasing image resolution (E) increases memory usage.


NEW QUESTION # 165
Consider the following Python code snippet using PyTorch Lightning and a Hugging Face Transformers model for multimodal classification. Which of the following code snippets is MOST appropriate to perform gradient accumulation in this context, assuming you want to accumulate gradients over 4 batches?

  • A.
  • B.
  • C.
  • D.

Answer: D

Explanation:
PyTorch Lightning provides a built-in argument in the 'Trainer' class to easily enable gradient accumulation. Setting will accumulate gradients over 4 batches before performing an optimizer step.


NEW QUESTION # 166
Assume you have trained a text-to-image diffusion model using a large dataset of landscape photographs. You now want to adapt this model to generate images of photorealistic portraits. Which of the following fine-tuning strategies is most likely to yield the best results with the least amount of training data and time?

  • A. Fine-tune only the CLIP model with portrait-related text descriptions and corresponding images.
  • B. Only fine tune the final layer of the IJ-Net model with the portrait dataset.
  • C. Fine-tune both the CLIP model and the U-Net architecture with the portrait dataset, using a smaller learning rate than the initial training.
  • D. Retrain the entire diffusion model from scratch using the portrait dataset.
  • E. Fine-tune the IJ-Net architecture of the diffusion model while keeping the CLIP model fixed.

Answer: C

Explanation:
Fine-tuning both the CLIP model and the IJ-Net architecture is the most effective approach. The CLIP model needs to learn the semantic relationship between portrait-related text and images, and the U-Net needs to adapt to generating portraits instead of landscapes. Using a smaller learning rate prevents overfitting and allows the model to leverage its existing knowledge from the landscape dataset. Retraining from scratch is wasteful, and fine-tuning only one component may not be sufficient for good performance. Simply fine-tuning the last layer will not change much.


NEW QUESTION # 167
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