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NVIDIA Generative AI Multimodal Sample Questions (Q238-Q243):

NEW QUESTION # 238
Consider the following PyTorch code snippet used for training a Generative A1 model:

A. The learning rate scheduler is not being used correctly.
B. The code is correct and will train the model efficiently.
C. The model parameters will not be updated correctly since optimizer.step() is called outside the loop.
D. The code will run, but it's computationally inefficient. Gradients should be zeroed before each backward pass.
E. CUDAOOM error because gradients are accumulating without updating parameters.

Answer: C,E

Explanation:

The code has two critical issues. First, 'optimizer.step()' is called only once per epoch after accumulating gradients from all batches. This is incorrect, as parameters aren't updated batch-wise. Second, is also called only once per epoch, meaning gradients from all batches accumulate. This will likely lead to a CUDAOOM error, especially for larger models.

NEW QUESTION # 239
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. Use a pre-trained image quality assessment model (e.g., BRISQUE, NIQE) to score each image and remove those with low scores.
B. Compute the image sharpness (e.g., using Laplacian variance) and remove images with low sharpness values.
C. Check for file corruption errors during image loading and remove those files.
D. Manually inspect each image and remove those that appear to be corrupted or low quality.
E. Calculate the average pixel intensity for each image and remove those with very low or very high average intensity.

Answer: A,B,C

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 # 240
You are working on a project to generate realistic images from text descriptions. You've trained a diffusion model, but the generated images often lack fine-grained details and exhibit artifacts. Which of the following techniques would be MOST effective in improving the image quality and fidelity?

A. Reduce the learning rate during training.
B. None of the above.
C. Implement classifier-free guidance, adjusting the guidance scale to balance fidelity and diversity.
D. Use a larger batch size during training.
E. Increase the number of diffusion steps during image generation.

Answer: C

Explanation:
Classifier-free guidance allows you to control the influence of the text description on the generated image. By adjusting the guidance scale, you can find a balance between generating images that are faithful to the text description and generating diverse and high-quality images. Increasing diffusion steps, batch size, or reducing the learning rate may help but is less targeted towards improving fidelity specifically.

NEW QUESTION # 241
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. Train the model with a larger dataset of higher-resolution images.
B. Increase the number of diffusion steps during the reverse process (sampling).
C. Reduce the batch size during inference.
D. Decrease the guidance scale (classifier-free guidance).
E. Use a simpler text encoder to reduce noise in the conditioning signal.

Answer: B

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 # 242
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?

A. Retrain the entire diffusion model from scratch using the portrait dataset.
B. Fine-tune both the CLIP model and the U-Net architecture with the portrait dataset, using a smaller learning rate than the initial training.
C. Only fine tune the final layer of the IJ-Net model with the portrait dataset.
D. Fine-tune only the CLIP model with portrait-related text descriptions and corresponding images.
E. Fine-tune the IJ-Net architecture of the diffusion model while keeping the CLIP model fixed.

Answer: B

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 # 243
......

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Marvelous NVIDIA NCA-GENM Pdf Pass Leader Are Leading Materials & Verified NCA-GENM: NVIDIA Generative AI Multimodal

NVIDIA NCA-GENM Dumps Get Success NVIDIA NCA-GENM Minimal Effort

NVIDIA Generative AI Multimodal Sample Questions (Q238-Q243):

NVIDIA Generative AI Multimodal pdf vce dumps & NCA-GENM free download training collection

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