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NVIDIA Generative AI Multimodal Sample Questions (Q360-Q365):

NEW QUESTION # 360
Consider the following Python code snippet using PyTorch. What does this code do in the context of data preprocessing for a Generative AI model?

Answer: A

Explanation:
The code snippet first resizes the images to a fixed size (256x256). Then, it converts the images into PyTorch tensors, which are the standard data format for PyTorch models. Finally, it normalizes the pixel values to a range of approximately [-1, 1]. This normalization helps to improve the training stability and performance of the generative A1 model by scaling the input values.

NEW QUESTION # 361
You are using the Stable Diffusion model for image generation. You want to generate an image of a 'cat wearing a hat in a cyberpunk city', but you are not satisfied with the initial results. Which of the following techniques could you use to refine the generated image and get closer to your desired outcome?

A. Increase the number of inference steps.
B. Decrease the CFG (Classifier-Free Guidance) scale.
C. Use a negative prompt to exclude unwanted elements or styles.
D. Change the random seed to explore different variations.
E. Reduce the number of inference steps.

Answer: A,C,D

Explanation:
Increasing the number of inference steps allows the diffusion process to refine the image more thoroughly. Using a negative prompt helps to guide the generation process by specifying what not to include in the image. Changing the random seed allows you to explore different variations of the same prompt, which can lead to more desirable results. Decreasing the CFG scale can reduce adherence to the prompt, and reducing the number of inference steps results in less refined images.

NEW QUESTION # 362
A multimodal A1 model is designed to translate sign language videos into text. The model performs well on videos with clear hand gestures and lighting conditions but struggles with videos recorded in low light or with partial hand occlusions. Which of the following strategies would be MOST effective in improving the model's robustness to these challenging conditions?

A. Reducing the frame rate of the input videos.
B. Applying image enhancement techniques (e.g., contrast adjustment, noise reduction) to the video frames.
C. Training the model on a smaller dataset.
D. Using a simpler text encoder.
E. Increasing the size of the text vocabulary.

Answer: B

Explanation:
Applying image enhancement techniques to the video frames can improve the visibility of hand gestures in low-light conditions and reduce the impact of noise, making the model more robust. Reducing the frame rate or training on a smaller dataset would likely decrease performance. Increasing the text vocabulary or using a simpler text encoder would not directly address the issue of poor video quality.

NEW QUESTION # 363
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. Increase the capacity of the encoder and decoder networks by adding more layers or units.
B. Decrease the batch size to reduce computational complexity
C. Decrease the weight of the Kullback-Leibler (KL) divergence term in the loss function-
D. Increase the dimensionality of the latent space
E. Use a more powerful decoder architecture, such as one with deconvolutional layers.

Answer: A,C,D,E

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 # 364
You are working on a project that involves generating realistic images from text descriptions using a diffusion model. You want to reduce the inference time of the model, which currently takes several minutes to generate a single image. Which of the following techniques would be MOST effective for accelerating inference without significantly compromising image quality?

A. Switching to a CPU-based inference engine.
B. Using a smaller batch size during inference.
C. Increasing the number of diffusion steps.
D. Employing techniques like DDIM (Denoising Diffusion Implicit Models) or progressive distillation to reduce the number of sampling steps required.
E. Training the diffusion model with a larger dataset.

Answer: D

Explanation:
DDIM and progressive distillation are specifically designed to reduce the number of sampling steps needed in diffusion models, leading to faster inference. Increasing diffusion steps (A) would increase inference time. Smaller batch size (B) might reduce memory usage, but not significantly affect overall inference time. Training with a larger dataset (D) improves quality, but not inference speed. Switching to CPU (E) would dramatically slow down inference.

NEW QUESTION # 365
......

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Reliable NVIDIA NCA-GENM Test Dumps & NCA-GENM Practice Test Pdf

NCA-GENM Practice Test Pdf - NCA-GENM Latest Braindumps Questions

NVIDIA Generative AI Multimodal Sample Questions (Q360-Q365):

The Best Accurate Reliable NCA-GENM Test Dumps & Passing NCA-GENM Exam is No More a Challenging Task

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