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100% Pass Quiz NVIDIA - NCA-GENM - Pass-Sure Exam NVIDIA Generative AI Multimodal Consultant

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NVIDIA Generative AI Multimodal Sample Questions (Q293-Q298):

NEW QUESTION # 293
When building a multimodal model using transformers, you observe that the model struggles to attend to the correct image regions when generating text descriptions. Which of the following techniques could you employ to improve the attention mechanism in the model?

A. Implement a visual attention mechanism that explicitly guides the model to focus on relevant image regions.
B. Decrease the learning rate.
C. Increase the number of transformer layers in the text encoder.
D. Reduce the dimensionality of the image features.
E. Use a larger batch size during training.

Answer: A

Explanation:
A visual attention mechanism can explicitly guide the model to attend to the relevant image regions. Increasing transformer layers only for text encoding wouldn't fix image attention. The other solutions are less helpful. By explicitly using the attention mechanism to focus on relavent image regions, the model would generate better text descriptions.

NEW QUESTION # 294
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. Use transfer learning by pre-training the audio component of the model on a large audio dataset.
C. Apply data augmentation techniques specifically designed for audio, such as adding noise or varying the speed and pitch.
D. Reduce the dimensionality of the audio features to simplify the learning task.
E. Decrease the weight of the audio modality in the loss function.

Answer: B,C

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 # 295
You're building a multimodal model to generate captions for videos. You've noticed that your model struggles to capture temporal relationships and sequential dependencies in the video frames. Which of the following architectures or techniques would be BEST suited to address this?

A. A 3D Convolutional Neural Network (3D CNN) that processes multiple frames as a volume.
B. Principal Component Analysis (PCA) to reduce dimensionality of each frame before feeding to the decoder
C. A standard Convolutional Neural Network (CNN) applied independently to each frame.
D. A Multilayer Perceptron (MLP) trained on flattened video frames.
E. A combination of a CNN to extract features from individual frames, followed by a Recurrent Neural Network (RNN) like LSTM or GRU to model temporal dependencies between the extracted features.

Answer: E

Explanation:
RNNs, specifically LSTMs and GRUs, are designed to handle sequential data and capture temporal relationships. Combining a CNN for feature extraction with an RNN allows the model to process individual frames and then model the dependencies between them. 3D CNNs can also capture temporal information, but can be computationally expensive. Other options don't explicitly address the temporal aspect.

NEW QUESTION # 296
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. Discard the blurry and low-resolution images from the dataset to ensure data quality.
B. Use a combination of image enhancement techniques and robust loss functions that are less sensitive to noisy data.
C. Increase the weight of the text data during model training to compensate for the noisy image data.
D. Apply image enhancement techniques such as sharpening and super-resolution to improve the quality of the blurry images.
E. Train the model on the noisy image data without any preprocessing or data augmentation.

Answer: B

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 # 297
You are tasked with deploying a generative A1 model for image inpainting using Triton Inference Server. The model takes an image with masked regions as input and outputs the completed image. You need to pre-process the input image before sending it to the server.
Which pre-processing steps are crucial for ensuring optimal performance and accuracy of the inpainting model?

A. Sharpening the image to enhance details.
B. Converting the image to grayscale.
C. Resizing the image to a fixed resolution and normalizing pixel values to a specific range (e.g., [0, 1] or [-1, 1]).
D. Applying data augmentation techniques like random rotations and flips.
E. Creating a binary mask indicating the regions to be inpainted.

Answer: C,E

Explanation:
Resizing and normalization ensure the input image conforms to the model's expected input size and data range. A binary mask is essential to indicate which regions the model should inpaint. Converting to grayscale would remove color information, hindering the inpainting process. Data augmentation should be done during the training phase. Sharpening is not typically a necessary pre-processing step for inpainting.

NEW QUESTION # 298
......

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