NVIDIA NCA-GENM VALID EXAM TEST & VCETORRENT - LEADING OFFER IN CERTIFICATION EXAMS PRODUCTS

NVIDIA NCA-GENM Valid Exam Test & VCETorrent - Leading Offer in Certification Exams Products

NVIDIA NCA-GENM Valid Exam Test & VCETorrent - Leading Offer in Certification Exams Products

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NVIDIA Generative AI Multimodal Sample Questions (Q254-Q259):

NEW QUESTION # 254
You are integrating a generative A1 model into a client's existing software infrastructure. The client is concerned about data privacy and security. What steps should you take during data gathering, deployment, and integration to address these concerns, while also using NVIDIA tools effectively?
Select all that apply:

  • A. Deploy the generative A1 model on-premises within the client's secure network, using Triton Inference Server to ensure controlled access and prevent data leakage.
  • B. Only utilize pre-trained open-source models
  • C. Implement differential privacy techniques during data collection and model training to protect sensitive information. Leverage NVIDIA's Merlin framework for privacy-preserving data preprocessing.
  • D. Avoid using any client data for training the generative A1 model, instead relying on publicly available datasets to minimize privacy risks.
  • E. Implement federated learning, training the generative A1 model on the client's data in a distributed manner without directly accessing or transferring the raw data. Use NVIDIA FLARE for orchestrating the federated learning process.

Answer: A,C,E

Explanation:
Differential privacy (A) adds noise to the data to protect individual records. On-premises deployment (B) maintains control over data access. Federated learning (D) trains the model on decentralized data without centralizing it. Avoiding client data entirely (C) may limit the model's effectiveness. NVIDIA Merlin and FLARE are tools that provide methods to create safe and private architecture. (E) is not always the best approach since the model might be very generalized and not adapted to specific tasks.


NEW QUESTION # 255
You're building a system that takes a medical image (e.g., X-ray) and a patient's medical history (text) as input, predicting the likelihood of a specific disease. You want to use SHAP (SHapley Additive exPlanations) values to explain the model's predictions. How would you adapt SHAP to handle both image and text inputs effectively?

  • A. Use DeepExplainer for the image component and a simple linear SHAP explainer for the text.
  • B. Treat the image and text as separate models and explain each independently.
  • C. Represent both the image and text as numerical vectors and then apply a standard SHAP explainer.
  • D. Use a multimodal SHAP implementation that is designed to handle both image and text features simultaneously, considering their interaction.
  • E. Apply KernelSHAP separately to the image and text, then combine the results.

Answer: D

Explanation:
The best approach is to use a multimodal SHAP implementation that considers the interaction between image and text features. This ensures a holistic explanation of the model's prediction based on both modalities. Treating them separately or simply concatenating features ignores potential synergistic effects.


NEW QUESTION # 256
Consider the following scenario: You're training a GAN for generating high-resolution images (e.g., 1024x1024). You notice that the training process is unstable, with the generator and discriminator constantly oscillating. Which of the following architectural modifications and training techniques could help stabilize the training process?

  • A. Replacing standard convolutional layers with transposed convolutional layers in the generator.
  • B. Using Wasserstein GAN (WGAN) with gradient penalty (GP).
  • C. Applying batch normalization in both the generator and discriminator.
  • D. Using ReLU activation functions in the discriminator.
  • E. Increasing the learning rate of both the generator and discriminator.

Answer: B,C

Explanation:
WGAN with gradient penalty (GP) addresses the instability caused by the Jensen-Shannon divergence used in standard GANs. Batch normalization can help stabilize training by reducing internal covariate shift. Transposed convolutions are a common practice but don't inherently stabilize training. Increasing the learning rate can exacerbate instability. ReLU activation can lead to vanishing gradients.


NEW QUESTION # 257
You are tasked with evaluating a multimodal A1 model that combines image and text inputs to generate product descriptions. You observe that the model performs well on common product categories (e.g., clothing, electronics) but struggles with niche categories (e.g., antique furniture, scientific instruments). Which of the following strategies would be MOST effective in improving the model's performance on niche categories?

  • A. Fine-tune the model on a dataset specifically curated for niche product categories.
  • B. Replace the image encoder with a more powerful architecture.
  • C. Implement data augmentation techniques to create synthetic data for niche categories.
  • D. Decrease the learning rate during training.
  • E. Increase the overall size of the training dataset.

Answer: A

Explanation:
Fine-tuning on a niche dataset addresses the specific lack of knowledge about those categories. While other options might offer marginal improvements, targeted fine-tuning is the most direct and effective approach. Data augmentation (E) could help, but is secondary to using real-world data for fine-tuning.


NEW QUESTION # 258
Consider the following Python code snippet using PyTorch, intended for fusing image and text features in a multimodal model. Assume 'image_featureS and 'text_features' are tensors of shape Which of the following fusion methods is implemented in this code?

  • A. Cross-modal Attention
  • B. Element-wise Addition
  • C. Tensor Product
  • D. Concatenation
  • E. Gated Attention

Answer: D

Explanation:
The code snippet uses 'torch.cat((image_features, text_features), dim=l y , which concatenates the image and text features along the feature dimension (dim=l This is a concatenation-based fusion method. Addition would involve 'image_features + text_featureS. Gated or cross-modal attention would involve learnable weights and attention mechanisms. Tensor product involves reshaping and matrix multiplication of the features. The given answer is A, but the code snippet is missing. Please provide the code for an accurate explanation.


NEW QUESTION # 259
......

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