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NVIDIA Generative AI Multimodal Sample Questions (Q308-Q313):
NEW QUESTION # 308
You're building a multimodal sentiment analysis model using text and audio dat a. You observe that the model's performance is significantly worse on audio samples from noisy environments. Which of the following data augmentation techniques would be MOST effective for improving the model's robustness to noisy audio?
Answer: A
Explanation:
Mixing different types of noise at varying SNRs is the most effective data augmentation technique for improving robustness to noisy audio because it directly simulates the real-world noise conditions that the model will encounter during deployment This helps the model learn to extract meaningful features from noisy audio. Adding noise to the text data is not relevant to the audio noise issue_ Time stretching and pitch shifting are useful for augmenting audio data, but they do not directly address the noise problem. Random cropping is primarily for image data.
NEW QUESTION # 309
Consider the following code snippet used in training a multimodal model:
During experimentation, you discover that the image modality contributes negligibly to the final prediction. How would you modify the training loop to dynamically adjust the importance of each modality?
Answer: E
Explanation:
Dynamically scaling gradients based on their magnitude allows the model to automatically adjust the importance of each modality during training. If the image gradients are small compared to the text gradients, the scaling factor will increase their influence, encouraging the model to learn from the image modality. Modality dropout is helpful, however gradient scaling provides finer control.
NEW QUESTION # 310
Assume you have trained a text-to-image diffusion model using a large dataset of landscape photographs. You now want to adapt this model to generate images of photorealistic portraits. Which of the following fine-tuning strategies is most likely to yield the best results with the least amount of training data and time?
Answer: C
Explanation:
Fine-tuning both the CLIP model and the IJ-Net architecture is the most effective approach. The CLIP model needs to learn the semantic relationship between portrait-related text and images, and the U-Net needs to adapt to generating portraits instead of landscapes. Using a smaller learning rate prevents overfitting and allows the model to leverage its existing knowledge from the landscape dataset. Retraining from scratch is wasteful, and fine-tuning only one component may not be sufficient for good performance. Simply fine-tuning the last layer will not change much.
NEW QUESTION # 311
You are building a Generative A1 model that generates captions for images. You want to evaluate the quality of the generated captions.
Which evaluation metrics are MOST suitable for this task?
Answer: E
Explanation:
BLEU, ROUGE, and CIDEr are standard metrics used for evaluating the quality of generated text, particularly in image captioning and machine translation. These metrics compare the generated captions to reference captions and measure the similarity in terms of n-grams, word overlap, and other features. Other options are used for Classification problems (Accuracy Precision, Fl-score, AUC) and Regression Problems (MSE, RMSE).
NEW QUESTION # 312
You are building a system that identifies objects in images based on spoken commands. You have trained a model but notice that it performs poorly when the spoken command contains synonyms or paraphrases of the training data. Which of the following techniques would BEST address this issue?
Answer: B
Explanation:
Option C is the most effective solution. Word embeddings or contextual embeddings capture the semantic meaning of words and phrases, enabling the model to understand synonyms and paraphrases. Options A, B, D, and E address different issues but are not directly related to the semantic understanding of spoken commands. Increasing data size (A) might help, but semantic understanding is key. Image augmentation (B) is irrelevant to the text understanding. Simplifying commands (D) limits the system's capabilities. Reducing learning rate (E) might improve training stability but doesn't address the semantic understanding.
NEW QUESTION # 313
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