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NVIDIA Generative AI Multimodal Sample Questions (Q196-Q201):

NEW QUESTION # 196
Consider the following scenario: You're training a GAN for generating high-resolution images (e.g., 1024x1024). You notice that the training process is unstable, with the generator and discriminator constantly oscillating. Which of the following architectural modifications and training techniques could help stabilize the training process?

A. Increasing the learning rate of both the generator and discriminator.
B. Replacing standard convolutional layers with transposed convolutional layers in the generator.
C. Applying batch normalization in both the generator and discriminator.
D. Using Wasserstein GAN (WGAN) with gradient penalty (GP).
E. Using ReLU activation functions in the discriminator.

Answer: C,D

Explanation:
WGAN with gradient penalty (GP) addresses the instability caused by the Jensen-Shannon divergence used in standard GANs. Batch normalization can help stabilize training by reducing internal covariate shift. Transposed convolutions are a common practice but don't inherently stabilize training. Increasing the learning rate can exacerbate instability. ReLU activation can lead to vanishing gradients.

NEW QUESTION # 197
You're using a diffusion model to generate high-resolution images. You notice that the generated images often contain artifacts and inconsistencies. Which of the following techniques could help improve the image quality?

A. Increasing the number of diffusion steps during training.
B. Using a smaller image size during training.
C. Employing classifier-free guidance during sampling.
D. Training with a larger batch size.
E. Decreasing the number of diffusion steps during sampling.

Answer: A,C

Explanation:
Increasing the number of diffusion steps allows the model to gradually refine the image and reduce artifacts. Classifier-free guidance provides a way to control the generation process and improve image quality by conditioning on a specific class or attribute. Training with a larger batch size may improve training stability but doesn't directly address artifact reduction. A smaller image size will reduce computational cost but doesn't necessarily improve quality at the desired resolution. Decreasing the number of diffusion steps can lead to lower-quality images with more artifacts.

NEW QUESTION # 198
A research team has developed a novel multimodal model that fuses text, image, and audio dat a. They want to quantitatively evaluate the model's performance in comparison to several existing state-of-the-art models. Which of the following evaluation metrics would be MOST appropriate to assess the model's ability to generate coherent and relevant text descriptions based on the combined multimodal input?

A. Frechet Inception Distance (FID).
B. Structural Similarity Index Measure (SSIM).
C. Perplexity.
D. BLEU (Bilingual Evaluation Understudy) and ROIJGE (Recall-Oriented Understudy for Gisting Evaluation).
E. Inception Score.

Answer: D

Explanation:
BLEU and ROUGE are standard metrics for evaluating text generation tasks by comparing the generated text to reference texts. They assess the similarity and overlap in terms of n-grams. Perplexity measures the uncertainty of a language model. Inception Score and FID are used for evaluating image generation quality. SSIM measures the similarity between two images.

NEW QUESTION # 199
Which of the following is NOT a typical application or benefit of using U-Net architectures in generative AI, particularly within the context of image generation and manipulation?

A. Image segmentation and pixel-wise classification.
B. Image inpainting and super-resolution tasks.
C. Encoding high-dimensional text data for multimodal embeddings.
D. Medical image analysis, such as tumor detection.
E. Facilitating efficient feature extraction and upsampling for detailed image generation.

Answer: C

Explanation:
U-Nets are primarily used for image-to-image tasks like segmentation, inpainting, and super-resolution. They excel at processing and generating images, but are not directly involved in encoding text data. CLIP is used for that purpose.

NEW QUESTION # 200
Which of the following techniques can be used to reduce the computational cost and memory footprint of large language models (LLMs) during inference?

A. Increasing the model size
B. Adding more layers
C. Pruning
D. Knowledge Distillation
E. Quantization

Answer: C,D,E

Explanation:
Quantization reduces the precision of the model's weights and activations, which can significantly reduce memory footprint and speed up inference. Knowledge distillation involves training a smaller 'student' model to mimic the behavior of a larger 'teacher' model, reducing the computational cost. Pruning removes unimportant connections (weights) from the model, leading to a sparser network and lower computational requirements. Increasing the model size or adding more layers would increase the computational cost.

NEW QUESTION # 201
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NCA-GENM Valid Exam Pattern | Authentic NCA-GENM Exam Questions

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NVIDIA Generative AI Multimodal Sample Questions (Q196-Q201):

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