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NVIDIA Generative AI Multimodal Sample Questions (Q208-Q213):

NEW QUESTION # 208
You are working on a project that involves generating high-resolution images using a StyleGAN architecture. You observe that while the generated images are generally realistic, they often exhibit 'water droplet' artifacts. What could be a cause and solution to these artifacts?

A. The artifacts are due to mode collapse. Use more diverse training data.
B. A and C
C. The artifacts are a result of an unstable adversarial training process. Apply gradient penalty during training.
D. The artifacts are likely due to aliasing during upsampling in the generator. Use filtered upsampling or anti-aliasing techniques to mitigate this.
E. Increase the learning rate to avoid local minima.

Answer: B

Explanation:
Water droplet artifacts often arise from aliasing in the upsampling layers of the generator network. Filtered upsampling (e.g., using a blur kernel before upsampling) or explicit anti-aliasing layers can smooth out the image and reduce these artifacts. Instabilities during adversarial training may amplify the artifacts. Gradient penalty helps stabilize the discriminator, thus improving the overall training stability and results.

NEW QUESTION # 209
You're using Stable Diffusion with a custom prompt to generate images of landscapes. You notice that the generated images consistently lack detail and appear blurry, despite increasing the number of inference steps. Which of the following prompt engineering techniques, combined with appropriate parameter tuning, is MOST likely to address this issue and improve the image's sharpness and detail?

A. Using completely unrelated keywords to encourage the model to create something unique.
B. Adding keywords like 'photorealistic', 'high resolution', '8k', 'detailed', and adjusting the 'clip_skip' parameter.
C. Specifying 'oil painting' or another artistic style to mask the lack of detail.
D. Decreasing the 'guidance_scale' to allow for more creative freedom.
E. Using a very short and general prompt to allow the model more freedom.

Answer: B

Explanation:
Adding keywords specifically related to image quality ('photorealistic', 'high resolution', '8k', 'detailed') helps guide the model towards generating sharper and more detailed images. 'clip_skip' influences the model to incorporate more details into the output images. Adjusting 'clip_skip' along with quality prompt keywords will enhance the image quality.

NEW QUESTION # 210
You are working on a multimodal model for autonomous driving that uses lidar, camera, and radar dat a. During testing, you notice that the model performs poorly in adverse weather conditions (e.g., heavy rain, fog). Which of the following strategies could you implement to improve the model's robustness to these conditions?

A. Augment the training data with synthetically generated data representing adverse weather conditions.
B. Train separate models for different weather conditions and switch between them based on weather sensor readings.
C. Use domain adaptation techniques to bridge the gap between simulated and real-world data in adverse weather.
D. Reduce the model complexity to prevent overfitting to specific weather conditions.
E. Increase the learning rate during training when adverse weather data is present.

Answer: A,B,C

Explanation:
Augmenting the training data with synthetic data helps the model learn to recognize objects and features even in degraded visual conditions. Training separate models allows for specialized processing tailored to each weather type. Domain adaptation techniques can help transfer knowledge learned in simulated environments to real-world adverse weather conditions. Increasing the learning rate specifically for adverse weather data (Option D) could be helpful but requires careful tuning to avoid instability. Reducing model complexity (Option E) might help prevent overfitting, but it could also reduce the model's ability to learn complex features relevant to all conditions.

NEW QUESTION # 211
Which of the following are key benefits of using multimodal learning compared to unimodal learning? (Select TWO correct answers)

A. Reduced computational complexity.
B. Enhanced ability to capture complex relationships between different data types.
C. Improved robustness to noise and missing data in one modality.
D. Simpler model architectures.
E. Guaranteed perfect accuracy.

Answer: B,C

Explanation:
Multimodal learning leverages information from multiple modalities, which can lead to improved robustness because the model can rely on other modalities when one is noisy or incomplete. It also allows the model to learn more complex relationships that might not be apparent from a single modality.

NEW QUESTION # 212
Consider the following Python code snippet using PyTorch Lightning and a Hugging Face Transformers model for multimodal classification. Which of the following code snippets is MOST appropriate to perform gradient accumulation in this context, assuming you want to accumulate gradients over 4 batches?

Answer: A

Explanation:
PyTorch Lightning provides a built-in argument in the 'Trainer' class to easily enable gradient accumulation. Setting will accumulate gradients over 4 batches before performing an optimizer step.

NEW QUESTION # 213
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NCA-GENM Dumps Free, NCA-GENM Reliable Test Prep

Pass Guaranteed Quiz 2025 NVIDIA Fantastic NCA-GENM: NVIDIA Generative AI Multimodal Dumps Free

NVIDIA Generative AI Multimodal Sample Questions (Q208-Q213):

High Pass-Rate NCA-GENM Dumps Free – Find Shortcut to Pass NCA-GENM Exam

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