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NVIDIA Generative AI Multimodal Sample Questions (Q184-Q189):

NEW QUESTION # 184
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 # 185
You are building a multi-modal model that combines text and image data for a search application. The goal is to retrieve relevant images given a text query. You have encoded both images and text into embeddings. What's a suitable loss function for training the model to ensure images relevant to a text query are ranked higher than irrelevant ones?

A. Contrastive Loss
B. Cross-entropy loss
C. Mean Squared Error (MSE)
D. KL Divergence
E. Triplet Loss

Answer: E

Explanation:
Triplet Loss is specifically designed for ranking tasks. It takes three inputs: an anchor (text query), a positive example (relevant image), and a negative example (irrelevant image). The loss function aims to minimize the distance between the anchor and the positive example while maximizing the distance between the anchor and the negative example. Contrastive loss works with pairs, not relative rankings. Cross-entropy, MSE, and KL Divergence are not suitable for ranking problems.

NEW QUESTION # 186
You are building a text-to-image application using CLIP. You notice that the generated images often lack specific details mentioned in the text prompt. Which of the following techniques would be most effective in improving the fidelity and detail of the generated images, given the limitations of CLIP's text encoder?

A. Reducing the number of training steps for the diffusion model to prevent overfitting to the training data and promote generalization.
B. Increasing the temperature parameter of the diffusion model used in conjunction with CLIP to introduce more randomness and potentially more detail.
C. Using a larger image decoder network with more parameters to add detail during the image generation process.
D. Applying prompt engineering techniques such as adding descriptive adjectives and context to the text prompt and fine-tuning the prompt with iterative feedback.
E. Training a custom text encoder from scratch with a larger dataset specifically tailored to your application's domain.

Answer: D

Explanation:
Prompt engineering is the most practical and effective method for improving the fidelity of text-to-image generation with CLIP, without requiring extensive retraining or architecture changes. By carefully crafting and refining the text prompt, you can guide the generation process to produce images that more accurately reflect the desired details. Training a custom text encoder (A) is resource-intensive. While a larger image decoder (B) might help, it doesn't address the core issue of accurately capturing the prompt's meaning. Increasing temperature (D) can add randomness but not necessarily detail. Reducing training steps (E) could worsen performance.

NEW QUESTION # 187
You're working on a project involving multimodal transfer learning for generating recipes from images of dishes and ingredient lists. You have a large dataset of images but a limited dataset of paired images and ingredient lists. You decide to leverage a pre-trained image model and a pre-trained text model. However, you are facing catastrophic forgetting after fine-tuning the models on the paired image and ingredient list dat a. Which of the following techniques would be MOST effective in mitigating catastrophic forgetting while adapting the pre-trained models to the new task?

A. Train the entire model from scratch on the limited paired dataset.
B. Increase the batch size during fine-tuning.
C. Freeze the weights of the pre-trained models and only train a small adapter module that bridges the gap between the pre-trained features and the recipe generation task.
D. Use a very high learning rate during fine-tuning.
E. Apply L1 regularization to the model weights.

Answer: C

Explanation:
Using adapter modules is a common technique to mitigate catastrophic forgetting. By freezing most of the pre-trained weights and only training a small adapter, you preserve the knowledge learned during pre-training while adapting the model to the new task. Training from scratch would negate the benefits of transfer learning. A high learning rate can exacerbate forgetting. L1 regularization can prevent overfitting but doesn't directly address forgetting. Increasing batch size might improve generalization but doesn't solve the core issue of catastrophic forgetting.

NEW QUESTION # 188
You are working on a multimodal sentiment analysis task where you have both textual reviews and corresponding product images. You want to build an attention mechanism to identify the most relevant parts of the image that contribute to the sentiment expressed in the text. Which of the following attention mechanisms is BEST suited for generating spatial attention maps highlighting these relevant regions in the image?

A. Spatial attention in the image encoder, conditioned on the text embedding (e.g., attention over image features based on text query).
B. Global average pooling of image features.
C. Channel attention in the image encoder (e.g., Squeeze-and-Excitation).
D. Self-attention in the text encoder (e.g., Transformer).
E. Temporal attention in a video encoder.

Answer: A

Explanation:
Spatial attention, conditioned on the text embedding, directly addresses the task. This mechanism allows the model to focus on specific regions of the image that are most relevant to the sentiment expressed in the text. The text embedding acts as a 'query' to attend over the image features, generating a spatial attention map that highlights the contributing regions. Self attention in text (A) focuses on relationships within the text itself. Channel attention (B) focuses on feature channel importance, not spatial localization related to the text. Temporal attention (D) is irrelevant for static images. Global average pooling (E) loses spatial information.

NEW QUESTION # 189
......

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HIGH PASS-RATE NVIDIA NCA-GENM RELIABLE TEST TEST - NCA-GENM FREE DOWNLOAD

High Pass-Rate NVIDIA NCA-GENM Reliable Test Test - NCA-GENM Free Download