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NVIDIA Generative AI Multimodal Sample Questions (Q60-Q65):

NEW QUESTION # 60
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. All of the above.
B. Increase the number of diffusion steps during the image generation process.
C. Fine-tune the CLIP model on a dataset of text-image pairs relevant to your desired domain.
D. Use negative prompt engineering to guide the diffusion process away from undesired attributes.
E. Increase the number of layers in the I-I-Net architecture of the diffusion model.

Answer: A

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 # 61
You have trained a text-to-image diffusion model. During inference, you notice that the generated images often lack fine-grained details and appear blurry. Which of the following techniques could you apply to improve the image quality without retraining the model?

A. Use a larger batch size during inference.
B. Reduce the learning rate during sampling.
C. Increase the model's capacity by adding more layers.
D. Decrease the guidance scale during sampling.
E. Increase the number of diffusion steps during sampling.

Answer: E

Explanation:
Increasing the number of diffusion steps during sampling allows the model to refine the generated image more thoroughly, leading to finer details and reduced blurriness. The guidance scale controls how closely the generated image adheres to the input text prompt; increasing it typically improves adherence but can sometimes reduce diversity. Batch size primarily affects computational efficiency. Reducing the learning rate is relevant during training, not inference. Adding model layers requires retraining.

NEW QUESTION # 62
You are evaluating a multimodal model that generates descriptions for video clips. You have human ratings for the relevance, fluency, and coherence of the generated descriptions. Which statistical test is MOST appropriate for determining if there is a statistically significant difference in the median ratings for each of these criteria (relevance, fluency, coherence) between two different versions of your model?

A. Mann-Whitney U test
B. ANOVA
C. Kruskal-Wallis test
D. T-test
E. Friedman Test

Answer: A

Explanation:
Since you're interested in comparing the medians of the ratings and not assuming a normal distribution (which is often the case with subjective human ratings), a non-parametric test is more appropriate than a t-test or ANOVA. The Mann-Whitney U test (also known as the Wilcoxon rank-sum test) is used to compare the medians of two independent groups. The Kruskal-Wallis test is used when you have more than two groups.

NEW QUESTION # 63
Which of the following are key architectural features of a U-Net that make it suitable for image generation tasks, particularly when starting from pure noise?

A. Skip connections between corresponding encoder and decoder layers.
B. A fully connected layer at the end for classification.
C. Progressive upsampling in the decoder path to reconstruct the image.
D. Use of convolutional layers in both encoder and decoder paths.
E. A bottleneck layer that compresses the encoded information.

Answer: A,C,D,E

Explanation:
U-Nets excel at image generation due to their skip connections, convolutional layers, bottleneck layer, and upsampling techniques. Skip connections preserve fine-grained details from earlier encoder layers during decoding. Convolutional layers extract spatial features. The bottleneck compresses information, and upsampling reconstructs the image to the original resolution. Fully connected layers are generally not used for image generation but for classification.

NEW QUESTION # 64
Given the following Python code snippet using Pandas, which is intended to filter rows where the 'price' column is greater than 100 and the 'quantity' column is less than 5, identify the correct approach to achieve this:

Answer: A,E

Explanation:
Option B uses the 'query' method for concise filtering. Option D correctly uses boolean indexing with the (and) operator within square brackets. Option E is syntactically incorrect because it uses the 'and' keyword, which is meant for single boolean values, not Pandas Series. Option A does logical OR which is not what we intend to filter. Option C is incorrecrt since filter method expects list of column names.

NEW QUESTION # 65
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Newest NCA-GENM Reliable Test Questions & Leader in Qualification Exams & Free Download NVIDIA NVIDIA Generative AI Multimodal

NVIDIA - Unparalleled NCA-GENM Reliable Test Questions

NVIDIA Generative AI Multimodal Sample Questions (Q60-Q65):

2025 NCA-GENM – 100% Free Reliable Test Questions | Perfect NCA-GENM Study Materials

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