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Relevant NCA-GENM Questions, New NCA-GENM Test Notes

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NVIDIA Generative AI Multimodal Sample Questions (Q166-Q171):

NEW QUESTION # 166
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. Employing classifier-free guidance during sampling.
B. Decreasing the number of diffusion steps during sampling.
C. Using a smaller image size during training.
D. Increasing the number of diffusion steps during training.
E. Training with a larger batch size.

Answer: A,D

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 # 167
You are building a Generative A1 application that processes images and text. The image data has missing pixel values, and the text data contains inconsistencies in abbreviations. Which data preprocessing techniques are MOST suitable to address these issues effectively?

A. Image: Deleting rows with missing pixel values; Text: Removing all abbreviations from the text data.
B. Image: Median imputation for missing pixels; Text: Using a fuzzy matching algorithm to correct inconsistencies in abbreviations.
C. Image: Replacing missing pixels with zero; Text: Ignoring abbreviations during analysis.
D. Image: Mean imputation for missing pixels; Text: Standardizing abbreviations using a predefined mapping.
E. Image: KNN imputation for missing pixels; Text: Applying regular expressions to expand abbreviations.

Answer: B,E

Explanation:
KNN imputation is more robust than mean imputation for images as it considers neighboring pixels. Regular expressions and fuzzy matching provide more accurate abbreviation handling compared to simply removing or ignoring them. KNN imputation and Median imputations both can work well. Fuzzy Matching can also resolve ambiguities in abreviations

NEW QUESTION # 168
You are training a text-to-image diffusion model and observe that the generated images often exhibit a 'washed-out' or overly smooth appearance. Which of the following adjustments to the training process would likely improve the image quality and detail?

A. Reduce the batch size used during training to minimize memory consumption.
B. Apply more aggressive data augmentation techniques to the training dataset.
C. Increase the weight of the perceptual loss function in the training objective.
D. Decrease the number of diffusion steps used during training.
E. Reduce the learning rate for the U-Net architecture within the diffusion model.

Answer: C

Explanation:
A perceptual loss function encourages the generated images to have more realistic features and details, as it compares the high- level representations of the generated images to the real images. Increasing its weight in the training objective would incentivize the model to produce more detailed and visually appealing results. Decreasing diffusion steps leads to faster but often lower-quality results. Reducing batch size can affect training stability but doesn't directly address the 'washed-out' appearance. Data augmentation and learning rate adjustments may have some impact, but are less directly targeted at improving image detail.

NEW QUESTION # 169
You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?

A. Increase the training data size by collecting more data.
B. Increase the weight decay (L2 regularization).
C. Implement early stopping based on the validation loss.
D. Increase the learning rate.
E. Decrease the learning rate.

Answer: A,B,C,E

Explanation:
Decreasing the learning rate can help the model to converge to a better solution and avoid overfitting. Increasing the training data size provides the model with more examples to learn from, improving generalization. Early stopping prevents the model from training for too long and overfitting the training data. Increasing weight decay adds more regularization, preventing the model from learning overly complex patterns. Increasing the learning rate might worsen overfitting.

NEW QUESTION # 170
You're training a multimodal Generative A1 model that takes video and text as input to predict future frames of the video. You notice that the model generates plausible visual content but often fails to accurately reflect the actions described in the text. Which of the following techniques is MOST likely to improve the alignment between the generated video and the text description?

A. Decrease the resolution of the video frames.
B. Implement a contrastive learning objective that encourages similar embeddings for corresponding video frames and text descriptions.
C. Increase the frame rate of the training videos.
D. Using only pretrained model weights.
E. Use a larger vocabulary for the text encoder.

Answer: B

Explanation:
Contrastive learning directly encourages the model to learn a shared representation space where semantically similar video frames and text descriptions are close to each other, improving alignment. Increasing frame rate, vocabulary size, or decreasing video resolution will not directly address the alignment problem. Training the whole model is needed instead of using just pre-trained weights.

NEW QUESTION # 171
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