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NVIDIA Generative AI Multimodal Sample Questions (Q319-Q324):

NEW QUESTION # 319
You are training a Generative Adversarial Network (GAN) for image synthesis. The discriminator loss is consistently near zero while the generator loss fluctuates significantly. Which of the following is the most likely cause and the best approach to address it?

A. The generator is too weak; reduce its capacity to simplify the learning task.
B. The training data is insufficient; augment the dataset with more diverse images.
C. The learning rate for the discriminator is too high; decrease it substantially.
D. Mode collapse is occurring; implement techniques like mini-batch discrimination or spectral normalization.
E. The discriminator is too weak; increase its capacity by adding more layers or filters.

Answer: D

Explanation:
A discriminator loss near zero indicates it's easily distinguishing real from fake images. The fluctuating generator loss means it's struggling to fool the discriminator. This often signifies mode collapse, where the generator produces a limited variety of outputs. Techniques like mini-batch discrimination (allowing the discriminator to compare the diversity of generated samples) or spectral normalization (constraining the Lipschitz constant of the discriminator) can help prevent this.

NEW QUESTION # 320
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?

Answer: B

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 # 321
You're developing a multimodal model that combines text and audio for sentiment analysis. The text component is performing well, but the audio component contributes very little to the overall accuracy. What's the MOST likely reason and how could you address it?

A. The audio features are not properly aligned with the text features. Use a cross-modal attention mechanism to improve alignment.
B. The audio data is irrelevant. Remove the audio component entirely.
C. The audio data is not preprocessed correctly. Apply aggressive noise reduction techniques.
D. The audio data is too large. Downsample the audio data to reduce computational cost.
E. The text component is simply too dominant. Reduce the weight given to the text component in the final prediction.

Answer: A

Explanation:
Misalignment between audio and text features is a common problem in multimodal models. Cross-modal attention mechanisms allow the model to learn which parts of the audio are most relevant to specific parts of the text, improving the integration of information. While other options might offer minor improvements, they don't address the core issue of feature misalignment. Removing the audio component defeats the purpose of a multimodal model. Downsampling and noise reduction might help slightly, but won't solve a fundamental alignment problem.

NEW QUESTION # 322
You are deploying a Riva-based speech-to-text service in a production environment. You observe high latency and CPU utilization on your server Which of the following actions would be most effective in optimizing the performance of your Riva service?

A. Enabling batching and concurrency in the Riva server configuration to process multiple requests simultaneously.
B. Switching to a smaller, less accurate ASR model to reduce computational load.
C. Increasing the audio chunk size sent to the Riva server to reduce the number of requests.
D. Deploying the Riva server on a CPU-only instance to reduce cost.
E. Disabling automatic punctuation and capitalization to simplify the ASR process.

Answer: A

Explanation:
Enabling batching and concurrency is a key optimization strategy for Riva. It allows the server to process multiple audio streams simultaneously, maximizing GPU utilization and reducing overall latency. Switching to a smaller model (A) might reduce load but also decreases accuracy. Disabling punctuation (C) has a minor impact. Increasing audio chunk size (D) can help, but batching is more significant. Deploying on CPU (E) negates the benefits of Riva's GPU acceleration.

NEW QUESTION # 323
You are building a generative A1 model that combines text and image inputs to generate novel images. You have access to NVIDIA NeMo and want to leverage its pre-trained models and tools. Which NeMo modules or features would be MOST beneficial for this multimodal task? (Select all that apply)

A. NeMo's pre-trained language models for text understanding and feature extraction.
B. NeMo's support for PyTorch Lightning for efficient training and scaling.
C. NeMo's ASR models for processing text inputs.
D. NeMo's TTS models for generating image descriptions.
E. NeMo's core building blocks for constructing custom neural network architectures.

Answer: A,B,E

Explanation:
NeMo's core building blocks simplify the creation of complex neural networks. PyTorch Lightning integration streamlines the training process, and NeMo's pre-trained language models provide a strong foundation for understanding and processing text inputs. ASR and TTS are irrelevant for the pure text and image to image creation. NeMo's core building blocks are fundamental to the framework.

NEW QUESTION # 324
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Quiz NVIDIA - NCA-GENM Accurate Valid Exam Preparation

100% Pass Quiz NVIDIA - Perfect Valid Exam NCA-GENM Preparation

NVIDIA Generative AI Multimodal Sample Questions (Q319-Q324):

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