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NVIDIA Generative AI Multimodal Sample Questions (Q129-Q134):

NEW QUESTION # 129
During data analysis for a multimodal A1 project involving image and text data, you discover that the image dataset contains a large number of blurry or low-resolution images. The text data, however, is relatively clean and well-structured. What is the BEST approach to mitigate the impact of the noisy image data on the overall model performance?

A. Apply image enhancement techniques such as sharpening and super-resolution to improve the quality of the blurry images.
B. Train the model on the noisy image data without any preprocessing or data augmentation.
C. Increase the weight of the text data during model training to compensate for the noisy image data.
D. Use a combination of image enhancement techniques and robust loss functions that are less sensitive to noisy data.
E. Discard the blurry and low-resolution images from the dataset to ensure data quality.

Answer: D

Explanation:
A combination of image enhancement and robust loss functions provides the best approach. Image enhancement techniques can improve the quality of the blurry images, making them more informative for the model. Robust loss functions, such as Huber loss or Tukey's biweight loss, are less sensitive to outliers and noisy data, which can further mitigate the impact of the remaining noise. Discarding data (A) reduces the dataset size. Increasing the weight of text data (C) may lead to the model ignoring visual information. Training on raw noisy data (D) will severely impact the model's ability to learn correct mappings.

NEW QUESTION # 130
Which of the following losses are suitable and can be used when training a Variational Autoencoder (VAE)?

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

Answer: A,C,E

Explanation:
VAEs typically use a combination of reconstruction loss (cross-entropy or MSE, depending on the data type) to ensure the decoder can accurately reconstruct the input, and KL divergence loss to enforce a prior distribution on the latent space. Hinge loss is commonly used in SVMs and contrastive loss is used in siamese networks to learn embeddings. Reconstruction loss based on Cross-entropy is used for descrete value as in binary classification, and MSE is used to capture how different each generated pixel from the corresponding pixel, which encourages the network to create accurate reconstruction.

NEW QUESTION # 131
Which of the following statements are TRUE regarding the challenges of training multimodal machine learning models? (Select TWO)

A. Multimodal models are immune to the problem of overfitting due to the diverse nature of the input data.
B. Aligning data from different modalities with varying temporal resolutions (e.g., high-frame-rate video and low-frequency audio) is a significant challenge.
C. Multimodal models are generally easier to train than unimodal models due to the increased information available.
D. Handling missing modality data (e.g., missing image for a text input) requires specialized techniques.
E. All available open-source tools readily support multimodal architectures and loss functions, so there are no software-related challenges.

Answer: B,D

Explanation:
Multimodal models are harder to train than unimodal ones. Missing modality data requires specific handling. Different temporal resolutions need alignment. Overfitting remains a concern. Open source availability is improving, but multimodal specific support remains more niche than unimodal.

NEW QUESTION # 132
You are using a pre-trained language model for text classification. You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques could help improve the model's generalization ability? (Select TWO)

A. Applying data augmentation techniques (e.g., random synonym replacement, back-translation).
B. Increasing the learning rate.
C. Using weight decay (L2 regularization).
D. Decreasing batch size.
E. Decreasing the amount of training data.

Answer: A,C

Explanation:
Weight decay penalizes large weights, preventing the model from overfitting to the training data. Data augmentation increases the diversity of the training data, making the model more robust to variations in unseen data. Increasing the learning rate can lead to instability and overfitting. Decreasing the amount of training data reduces the model's ability to learn general patterns.

NEW QUESTION # 133
Consider the following code snippet, where you are trying to load image and text data for a multimodal model. What is the most likely cause of error if the code fails during the image loading step?

A. The learning rate is set too high.
B. The system doesn't have CUDA drivers installed.
C. The image files are corrupted or in an unsupported format.
D. The batch size is too large.
E. The text data is not in IJTF-8 encoding.

Answer: C

Explanation:
Since the error occurs specifically during the image loading step, the most likely cause is related to the image files themselves. Corrupted files or unsupported formats would prevent the image loading library from successfully reading the images. The other options are less likely to cause an error specifically during image loading.

NEW QUESTION # 134
......

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NCA-GENM Latest Braindumps, Study NCA-GENM Plan

Top NCA-GENM Latest Braindumps | Professional Study NCA-GENM Plan: NVIDIA Generative AI Multimodal

NVIDIA Generative AI Multimodal Sample Questions (Q129-Q134):

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