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NVIDIA Generative AI Multimodal Sample Questions (Q261-Q266):

NEW QUESTION # 261
Consider the following code snippet used for creating a multimodal dataset with PyTorch. The dataset contains images and corresponding text descriptions. However, during training, you observe a significant imbalance in the data distribution of text lengths. Which of the following techniques would BEST address this issue?

A. Applying standard image augmentation techniques to the image data.
B. Padding or truncating text sequences to a fixed length.
C. Using the exact same length of text and same images.
D. Using a learning rate scheduler to adjust the learning rate during training-
E. Applying Batch Normalization to the image features.

Answer: B

Explanation:
Padding or truncating text sequences to a fixed length is a standard technique for handling variable-length sequences in NLP tasks. This ensures that all text inputs have the same dimensionality, which is required for efficient batch processing in neural networks- While image augmentation can improve the model's robustness to variations in image data, it does not directly address the issue of text length imbalance. Learning rate scheduling and batch normalization are general training techniques that can improve convergence, but they do not specifically address the text length imbalance.

NEW QUESTION # 262
You are building a system that uses both video and text to determine the sentiment of movie reviews. You notice that while your system works great on the training set, the performance is much worse on the validation set. What is the MOST likely reason for this and what methods can you use to improve the performance?

A. The Video Data is too Large. Consider compressing the video data to ensure that it all fits into memory.
B. The model is not complex enough. Use a larger model or different model to improve results.
C. The text data is corrupt. Clean the text data by ensuring that the text is not noisy or missing.
D. The model is overfitting on the training data. Use regularization techniques or more training data to overcome this.
E. The training data is not representative enough of the real world. Gather new data that matches the real world, or introduce a cross validation training routine.

Answer: D,E

Explanation:
The most likely reason is that the data is overfitting and the model is not able to properly generalize to new data. Overfitting causes performance in the training set to be great but performance in the validation set to be poor. Regularization techniques (such as dropout, Ll or L2) can reduce this effect. The other likely reason is that the training data is not representative enough of the real world, as the data might not be realistic, too synthetic, or missing real world information.

NEW QUESTION # 263
You are training a Variational Autoencoder (VAE) and notice that the generated samples are blurry and lack detail. Which of the following adjustments could help improve the quality and sharpness of the generated images2 Select all that apply.

A. Use a more powerful decoder architecture, such as one with deconvolutional layers.
B. Increase the dimensionality of the latent space
C. Decrease the weight of the Kullback-Leibler (KL) divergence term in the loss function-
D. Increase the capacity of the encoder and decoder networks by adding more layers or units.
E. Decrease the batch size to reduce computational complexity

Answer: A,B,C,D

Explanation:
Increasing network capacity allows the model to learn more complex representations. Decreasing the KL divergence allows the decoder to focus more on reconstruction, potentially sacrificing some disentanglement. Increasing the latent space provides more room for capturing variations in the data. Using a more powerful decoder helps in generating sharper images

NEW QUESTION # 264
You have trained a multimodal model for visual question answering (VQA). During inference, the model often generates incorrect answers even though it seems to understand the question and the image content. Which of the following strategies could help improve the accuracy of the model's predictions? (Select all that apply)

A. Reduce the size of the training dataset.
B. Implement a loss function that penalizes incorrect answers more heavily.
C. Use beam search decoding to explore multiple possible answer sequences.
D. Apply data augmentation techniques to the training images, such as random cropping and rotations.
E. Increase the learning rate during fine-tuning.

Answer: B,C,D

Explanation:
Beam search can help explore more probable answer sequences, data augmentation can improve the model's robustness, and a loss function that penalizes incorrect answers more heavily can encourage the model to learn more accurate predictions. Increasing the learning rate might lead to instability, and reducing the dataset size is generally detrimental to performance.

NEW QUESTION # 265
Which of the following NVIDIA SDKs is most suitable for deploying a real-time, low-latency speech-to-text service as part of a multimodal AI application?

A. NVIDIA Tritone Inference Server
B. NVIDIAAvatar Cloud Engine (ACE)
C. CUDA Toolkit
D. NVIDIA NeMo.
E. NVIDIA Riva

Answer: E

Explanation:
NVIDIA Riva is specifically designed for building and deploying real-time conversational A1 services, including speech-to-text, text-to- speech, and natural language understanding. Its focus is on low latency and high accuracy for conversational applications.

NEW QUESTION # 266
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Valid NCA-GENM Exam Vce, NCA-GENM Exam Collection

NCA-GENM Exam Collection - Exam NCA-GENM Online

NVIDIA Generative AI Multimodal Sample Questions (Q261-Q266):

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