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NVIDIA Generative AI Multimodal Sample Questions (Q287-Q292):

NEW QUESTION # 287
Consider a scenario where you are developing a virtual assistant that can answer questions about images. You have a large dataset of images and corresponding question-answer pairs. Which architecture is BEST suited for this task?

A. A transformer-based model that processes both images and questions as sequences of tokens, allowing for attention-based interaction between modalities.
B. A simple feedforward neural network that takes flattened image pixels and question embeddings as input.
C. A combination of a pre-trained word embedding model (e.g., Word2Vec) for the question and a separate model for image classification.
D. A Support Vector Machine (SVM) trained on image features and question keywords.
E. A convolutional neural network (CNN) for image feature extraction followed by a recurrent neural network (RNN) for question encoding and a fully connected layer for answer prediction.

Answer: A

Explanation:
Option B, a transformer-based model, is the most suitable architecture for Visual Question Answering (VQA). Transformers excel at capturing long-range dependencies and interactions between different modalities (image and text) using attention mechanisms, leading to better performance than CNN-RNN combinations or simpler models.

NEW QUESTION # 288
You are tasked with fine-tuning a pre-trained multimodal model for a new task involving image and text inputs. The pre-trained model was trained on a large dataset of image-caption pairs. Which of the following strategies would be MOST effective for transfer learning in this scenario, considering computational efficiency and performance?

A. Use knowledge distillation to transfer knowledge from the pre-trained model to a smaller, more efficient model.
B. Fine-tune all layers of the pre-trained model with a very small learning rate.
C. Train a new model from scratch on the new task's dataset.
D. Fine-tune a subset of layers, specifically those responsible for feature extraction from both image and text modalities, while keeping the lower layers frozen.
E. Fine-tune only the classification head (output layer) while freezing all other layers of the pre-trained model.

Answer: D

Explanation:
Option C is the most effective strategy. Fine-tuning a subset of layers allows the model to adapt to the new task while leveraging the pre-trained knowledge. Freezing the lower layers preserves the general features learned from the large dataset, while fine-tuning the feature extraction layers allows the model to learn task-specific features. Fine-tuning all layers (Option B) can lead to overfitting and is computationally expensive. Freezing all layers except the classification head (Option A) may not provide sufficient adaptation. Training from scratch (Option D) is computationally expensive and requires a large dataset. Knowledge distillation (Option E) is also a valid option but may not be the most direct approach for transfer learning when the pre-trained model's architecture is suitable.

NEW QUESTION # 289
You are building a multimodal generative A1 model that combines text, images, and audio. You notice that the model performs well on text and images but struggles with audio, particularly in noisy environments. Which of the following strategies would be MOST effective in improving the model's performance with audio data?

A. Increase the learning rate for the audio modality during training.
B. Apply data augmentation techniques specifically designed for audio, such as adding noise or varying the speed and pitch.
C. Reduce the dimensionality of the audio features to simplify the learning task.
D. Decrease the weight of the audio modality in the loss function.
E. Use transfer learning by pre-training the audio component of the model on a large audio dataset.

Answer: B,E

Explanation:
Data augmentation (C) increases the robustness of the model to variations in audio, including noise. Transfer learning (E) allows the model to leverage knowledge from a large, pre-existing audio dataset, improving its initial performance.

NEW QUESTION # 290
You are working on a project that involves generating high-resolution images using a StyleGAN architecture. You observe that while the generated images are generally realistic, they often exhibit 'water droplet' artifacts. What could be a cause and solution to these artifacts?

A. The artifacts are likely due to aliasing during upsampling in the generator. Use filtered upsampling or anti-aliasing techniques to mitigate this.
B. The artifacts are due to mode collapse. Use more diverse training data.
C. Increase the learning rate to avoid local minima.
D. The artifacts are a result of an unstable adversarial training process. Apply gradient penalty during training.
E. A and C

Answer: E

Explanation:
Water droplet artifacts often arise from aliasing in the upsampling layers of the generator network. Filtered upsampling (e.g., using a blur kernel before upsampling) or explicit anti-aliasing layers can smooth out the image and reduce these artifacts. Instabilities during adversarial training may amplify the artifacts. Gradient penalty helps stabilize the discriminator, thus improving the overall training stability and results.

NEW QUESTION # 291
You are developing a text-to-image generation system using a diffusion model. During inference, you notice that the generated images often contain artifacts or inconsistencies. What is the most appropriate strategy to reduce these artifacts and improve the overall image quality?

A. Decrease the guidance scale (classifier-free guidance).
B. Train the model with a larger dataset of higher-resolution images.
C. Use a simpler text encoder to reduce noise in the conditioning signal.
D. Reduce the batch size during inference.
E. Increase the number of diffusion steps during the reverse process (sampling).

Answer: E

Explanation:
Increasing the number of diffusion steps allows the model to more accurately refine the image during the reverse diffusion process, leading to fewer artifacts and a smoother, more consistent output. Decreasing the guidance scale might reduce adherence to the text prompt. A simpler text encoder might reduce detail. While training with a larger dataset is always beneficial, it's not a direct solution to existing artifacts during inference. Batch size primarily impacts memory usage and throughput, not individual image quality.

NEW QUESTION # 292
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