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NVIDIA Generative AI Multimodal Sample Questions (Q295-Q300):

NEW QUESTION # 295
You are working with a large dataset of images to train a Generative A1 model. You suspect that some images are corrupted or of poor quality, which could negatively impact training. Which of the following methods would be the MOST effective in identifying and removing these problematic images?

A. Check for file corruption errors during image loading and remove those files.
B. Compute the image sharpness (e.g., using Laplacian variance) and remove images with low sharpness values.
C. Calculate the average pixel intensity for each image and remove those with very low or very high average intensity.
D. Use a pre-trained image quality assessment model (e.g., BRISQUE, NIQE) to score each image and remove those with low scores.
E. Manually inspect each image and remove those that appear to be corrupted or low quality.

Answer: A,B,D

Explanation:
Checking file integrity to remove corruption images is an important first step. Computing Image Sharpness is an effective way to programmatically identify and filter blur or out-of-focus Images. Using pre-trained image assessment models is another advanced and automativ approach to identifying and removing images of low quality, even if they are not overtly corrupted. Manually checking would take too long. Average pixel intensity is often useful to filter.

NEW QUESTION # 296
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. Converting the image to grayscale.
B. Creating a binary mask indicating the regions to be inpainted.
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. Sharpening the image to enhance details.

Answer: B,C

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 # 297
When training a multimodal model with both text and image data, what is a common challenge related to the different characteristics and scales of these modalities, and what are some common strategies to address it? (Select TWO correct answers)

A. Modalities often have different scales and distributions, leading to one modality dominating the learning process.
B. Always training the image processing part first and freezing the weights before text processing
C. Using modality-specific normalization techniques and carefully weighting the loss contributions from each modality.
D. Text data inherently contains more information than image data, making it difficult to balance their contributions.
E. Images are always processed faster than text, requiring artificial delays in the text processing pipeline.

Answer: A,C

Explanation:
Different modalities often have different scales and distributions, leading to one modality dominating the learning process. To address this, modality-specific normalization techniques (e.g., batch normalization for images and layer normalization for text) and carefully weighting the loss contributions from each modality are often used.

NEW QUESTION # 298
You're building a multimodal model that takes an image and a question as input and outputs an answer (Visual Question Answering - VQA). You find your model is heavily relying on the question type (e.g., 'What color is...' always predicts 'blue') and ignoring the image content. Select TWO of the following techniques that could help mitigate this 'language prior' problem.

A. Use a question-only baseline to explicitly measure the model's reliance on language priors and then penalize deviations from that baseline during training.
B. Decrease the learning rate of the image encoder.
C. Increase the training data size by including more diverse images.
D. Balance the dataset by ensuring an equal number of correct answers for each question type.
E. Replace the image encoder with a simpler architecture.

Answer: A,D

Explanation:
B and D are the best answers. Using a question-only baseline (B) allows you to directly quantify the model's reliance on language priors and then penalize the model for over-relying on them during training, encouraging it to pay more attention to the image. Balancing the dataset (D) by ensuring an equal number of correct answers for each question type makes it harder for the model to simply predict based on the question type alone. The image encoder shouldn't be replaced as that is needed in the task. More images wouldn't necessarily fix the data imbalance.

NEW QUESTION # 299
You are developing a text-to-image generation system using a diffusion model. During inference, you notice that the generated images often contain artifacts or inconsistencies. What is the most appropriate strategy to reduce these artifacts and improve the overall image quality?

A. Decrease the guidance scale (classifier-free guidance).
B. Use a simpler text encoder to reduce noise in the conditioning signal.
C. Reduce the batch size during inference.
D. Increase the number of diffusion steps during the reverse process (sampling).
E. Train the model with a larger dataset of higher-resolution images.

Answer: D

Explanation:
Increasing the number of diffusion steps allows the model to more accurately refine the image during the reverse diffusion process, leading to fewer artifacts and a smoother, more consistent output. Decreasing the guidance scale might reduce adherence to the text prompt. A simpler text encoder might reduce detail. While training with a larger dataset is always beneficial, it's not a direct solution to existing artifacts during inference. Batch size primarily impacts memory usage and throughput, not individual image quality.

NEW QUESTION # 300
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