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2026 NVIDIA Efficient Latest NCA-GENM Exam Discount

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Quiz 2026 NCA-GENM: Accurate Latest NVIDIA Generative AI Multimodal Exam Discount

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NVIDIA Generative AI Multimodal Sample Questions (Q304-Q309):

NEW QUESTION # 304
You are training a multimodal generative A1 model that takes text and images as input to generate videos. During experimentation, you observe that the model performs well on common scenarios (e.g., 'a dog playing in the park') but struggles to generate coherent videos for less frequent or abstract scenarios (e.g., 'the concept of time flowing'). What is the MOST effective strategy to improve the model's performance on these challenging scenarios, focusing on test data quality?

A. Reduce the complexity of the model architecture to prevent overfitting on the common scenarios.
B. Increase the size of the training dataset by duplicating existing common scenario examples.
C. Curate a new test dataset specifically containing challenging scenarios and use it to evaluate and fine-tune the model. Ensure the new test data includes diverse interpretations and variations of the abstract concepts.
D. Implement data augmentation techniques on the existing training data, focusing on color adjustments and minor image transformations.
E. Train the model for a significantly longer duration on the existing training data.

Answer: C

Explanation:
Curating a specific test dataset for challenging scenarios allows for targeted evaluation and fine-tuning. Duplicating common scenarios (A) won't address the problem. Simple augmentations (B) may not be sufficient. Reducing model complexity (D) might hurt overall performance. Longer training (E) without addressing data bias is unlikely to help significantly.

NEW QUESTION # 305
You are building a text-to-image generation pipeline using CLIP and a diffusion model. After training, you notice that the generated images often lack the specific details mentioned in the text prompts. Which of the following strategies could you employ to improve the alignment between text and image?

A. Increase the number of diffusion steps during the image generation process.
B. Increase the number of layers in the I-I-Net architecture of the diffusion model.
C. Fine-tune the CLIP model on a dataset of text-image pairs relevant to your desired domain.
D. All of the above.
E. Use negative prompt engineering to guide the diffusion process away from undesired attributes.

Answer: D

Explanation:
All the strategies mentioned can help improve the alignment between text and image. Increasing U-Net layers can improve image detail. Fine-tuning CLIP improves semantic understanding. Negative prompts refine image generation. More diffusion steps can improve image quality. All options contribute to better alignment.

NEW QUESTION # 306
You are working on a project that involves generating realistic images of furniture based on textual descriptions. The input data consists of text descriptions and a small dataset of existing furniture images. Which data augmentation techniques would be MOST effective in improving the quality and diversity of the generated images?

A. Combining A, B, and C.
B. Synthesizing new text descriptions using paraphrasing and back-translation techniques.
C. Focusing solely on increasing the size of the text dataset and ignoring image augmentation.
D. Randomly cropping and rotating the existing furniture images.
E. Using generative adversarial networks (GANs) to generate new furniture images from the existing dataset.

Answer: A

Explanation:
Combining all techniques provides the best results. Image augmentations like cropping and rotation increase the variance of the image data. GANs create entirely new images, and text augmentation enhances the diversity of the input descriptions. Focusing only on one modality will likely limit the model's performance.

NEW QUESTION # 307
Consider the following Python code snippet used for evaluating a generative model. What potential issue exists with this code, and how would you rectify it to ensure a robust evaluation?

A. The number of generated images Cn_generated_imageS) is too small for a reliable Inception Score calculation and should be increased to at least 50,000.
B. The code does not account for the stochastic nature of generative models and needs to be run multiple times with different random seeds to get a stable estimate of the Inception Score.
C. The Inception Score is not an appropriate metric for evaluating generative models, and other methods should be used instead.
D. The 'inception_score' function is deprecated and should be replaced with a newer metric like FID.
E. The code is fundamentally correct and does not have any issues.

Answer: B

Explanation:
Generative models produce different outputs each time due to their inherent stochasticity. Evaluating only once can result in a score that isn't representative. The code needs to be run multiple times with different random seeds, and the results averaged to obtain a more reliable Inception Score. While FID is a good metric (A), the core issue here is the lack of accounting for stochasticity. While a larger sample size can help improve reliability, addressing the randomness is most important. Inception score is a valid metric (D) although it has limitations. The given sample code is assumed to be incomplete, hence it will not have any issue on its own.

NEW QUESTION # 308
You're training a conditional GAN to generate images of birds based on text descriptions. The GAN generates images, but they lack fine- grained details and often have artifacts. Which of the following techniques are MOST likely to improve the quality and realism of the generated images? (Select TWO)

A. Using a deeper and wider generator network (e.g., with more layers and channels).
B. Using a more powerful discriminator architecture (e.g., with attention mechanisms).
C. Implementing spectral normalization in both the generator and discriminator.
D. Using a simple Multi-Layer Perceptron (MLP) as the generator.
E. Reducing the size of the input noise vector to the generator.

Answer: A,C

Explanation:
Spectral normalization helps stabilize training by limiting the Lipschitz constant of the discriminator and generator, preventing exploding gradients and improving image quality. A deeper and wider generator network can capture more complex image features and generate more detailed images. A simple MLP wouldn't be suitable for generating high-resolution images. Reducing the input noise vector size might limit the diversity of generated images. A more powerful discriminator helps in better distinguishing between real and fake images, which guides the generator to produce more realistic outputs. However, spectral normalization directly addresses stability issues that cause artifacts.

NEW QUESTION # 309
......

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