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NVIDIA Generative AI Multimodal Sample Questions (Q312-Q317):

NEW QUESTION # 312
You are fine-tuning a pre-trained large language model (LLM) for a specific text generation task using LoRA (Low-Rank Adaptation).
Which of the following statements accurately describes the benefits and limitations of using LoRA?

A. LoRA is not compatible with model parallelism techniques.
B. LoRA allows for efficient task switching by only storing and loading the small LoRA parameters for different tasks, while keeping the original LLM weights frozen.
C. A and B.
D. LoRA reduces the number of trainable parameters by inserting low-rank matrices into the original model layers, making fine-tuning more memory-efficient.
E. LoRA can improve the accuracy of the fine-tuned model compared to full fine-tuning by preventing overfitting.

Answer: C

Explanation:
LoRA significantly reduces the number of trainable parameters, enabling more memory-efficient fine-tuning, especially for large models. It also facilitates efficient task switching as only the small LoRA parameters need to be stored and loaded for different tasks. While LoRA can help prevent overfitting compared to full fine-tuning, it doesn't guarantee improved accuracy. LoRA can be effectively combined with model parallelism.

NEW QUESTION # 313
You are building a multimodal Generative AI model that takes text and images as input to generate a story. The text encoder uses a pre-trained BERT model, and the image encoder uses a pre-trained ResNet50 model. What is the BEST strategy to align the feature spaces of these two encoders during training to ensure effective multimodal fusion?

A. Use a contrastive loss function that encourages similar representations for semantically related text and images, and dissimilar representations otherwise. Fine-tune BERT and ResNet50.
B. Concatenate the outputs of BERT and ResNet50 directly without any alignment strategy.
C. Fine-tune only the BERT model while keeping the ResNet50 model frozen.
D. Fine-tune only the ResNet50 model while keeping the BERT model frozen.
E. Train a separate linear projection layer for each encoder and minimize the LI distance between the projected features. Freeze BERT and ResNet50.

Answer: A

Explanation:
Contrastive learning is a powerful technique for aligning feature spaces in multimodal learning. By encouraging similar representations for semantically related inputs and dissimilar representations for unrelated inputs, it allows the model to learn a shared representation space that facilitates effective fusion. Fine- tuning both encoders allows for adaptation to the specific task. Other methods are less effective for aligning high-dimensional feature spaces from different modalities.

NEW QUESTION # 314
You are fine-tuning a large pre-trained language model for a specific downstream task using a limited amount of training dat a. Which of the following techniques is MOST likely to prevent overfitting and improve the model's generalization performance?

A. Increasing the batch size as much as possible to maximize GPU utilization.
B. Using a very large learning rate during fine-tuning.
C. Removing all regularization techniques to allow the model to perfectly fit the training data.
D. Training the entire model from scratch using the limited training data.
E. Applying aggressive weight decay and dropout regularization.

Answer: E

Explanation:
Overfitting occurs when a model learns the training data too well and fails to generalize to unseen data. Aggressive weight decay and dropout are regularization techniques that penalize complex models and prevent them from memorizing the training data. Training from scratch with limited data will almost certainly lead to overfitting. A large learning rate can also exacerbate overfitting. While a larger batch size can improve training efficiency, it doesn't directly address overfitting.

NEW QUESTION # 315
You are building a Generative Adversarial Network (GAN) to generate high-resolution images. The generated images suffer from mode collapse, where the generator only produces a limited variety of images. Which of the following techniques would be MOST effective in mitigating mode collapse?

A. Using a simpler generator architecture.
B. Using mini-batch discrimination or feature matching in the discriminator.
C. Increasing the learning rate of the generator.
D. Reducing the size of the generator's latent space.
E. Decreasing the learning rate of the discriminator.

Answer: B

Explanation:
Mini-batch discrimination and feature matching encourage the generator to produce diverse images by considering the relationships between generated samples in a batch. Adjusting learning rates may have some effect but doesn't directly address the diversity issue. Reducing the latent space or using a simpler generator would likely reduce the generator's capacity and exacerbate mode collapse.

NEW QUESTION # 316
You are tasked with optimizing a Generative A1 model that processes both image and text dat a. The current model uses a simple concatenation of image features (extracted from a ResNet-50) and text embeddings (from BERT) as input to a transformer. You observe that the model struggles to generate coherent descriptions for complex images. Which of the following optimization strategies would be MOST effective in improving the model's understanding of the multimodal input?

A. Augment the text data with more examples.
B. Replace concatenation with a cross-attention mechanism between image features and text embeddings.
C. Reduce the learning rate by a factor of 10.
D. Switch to a larger ResNet architecture (e.g., ResNet-101 ) while keeping the concatenation.
E. Increase the size of the transformer encoder layers.

Answer: B

Explanation:
Cross-attention allows the model to learn which parts of the image are most relevant to each word in the text, enabling a more nuanced understanding of the relationship between the two modalities. Concatenation treats all features equally, which is less effective. Increasing transformer size or ResNet architecture might help but doesn't address the core issue of multimodal interaction.

NEW QUESTION # 317
......

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