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NVIDIA Generative AI Multimodal Sample Questions (Q288-Q293):

NEW QUESTION # 288
You are designing a IJ-Net architecture for semantic segmentation of medical images. Your input images are 512x512 with 3 channels.
You want to ensure the final output segmentation map is also 512x512. Which of the following design choices are crucial for achieving this resolution, considering the downsampling and upsampling stages?

A. Using only strided convolutions for downsampling and transposed convolutions for upsampling without skip connections.
B. Using max pooling with a kernel size of 3x3 and stride of 2 for downsampling, and nearest neighbor interpolation for upsampling.
C. Using a batch size of 1 during training to simplify memory management.
D. Employing only IXI convolutions in the bottleneck of the U-Net architecture to reduce computational complexity.
E. Ensuring that the number of downsampling and upsampling blocks are equal, and employing skip connections from corresponding encoder layers to decoder layers.

Answer: E

Explanation:
Maintaining the same resolution in IJ-Net requires symmetric downsampling and upsampling and the crucial use of skip connections. These connections pass feature maps from the downsampling (encoder) path to the corresponding layers in the upsampling (decoder) path, allowing the decoder to recover spatial information lost during downsampling. Option A omits skip connections. Option C's nearest neighbor interpolation can lead to blocky artifacts. Option D describes a bottleneck optimization unrelated to output resolution. Option E is about training parameters.

NEW QUESTION # 289
You are developing a text-to-image generation system using a diffusion model. During inference, you notice that the generated images often contain artifacts or inconsistencies. What is the most appropriate strategy to reduce these artifacts and improve the overall image quality?

A. Reduce the batch size during inference.
B. Use a simpler text encoder to reduce noise in the conditioning signal.
C. Decrease the guidance scale (classifier-free guidance).
D. Increase the number of diffusion steps during the reverse process (sampling).
E. Train the model with a larger dataset of higher-resolution images.

Answer: D

Explanation:
Increasing the number of diffusion steps allows the model to more accurately refine the image during the reverse diffusion process, leading to fewer artifacts and a smoother, more consistent output. Decreasing the guidance scale might reduce adherence to the text prompt. A simpler text encoder might reduce detail. While training with a larger dataset is always beneficial, it's not a direct solution to existing artifacts during inference. Batch size primarily impacts memory usage and throughput, not individual image quality.

NEW QUESTION # 290
You're developing a multimodal A1 system that takes image data, text descriptions, and user interaction data (clicks, dwell time) to generate personalized product recommendations. To effectively combine these modalities and capture complex relationships, which model architecture would be most suitable?

A. A decision tree-based model.
B. A simple linear regression model.
C. A k-nearest neighbors (KNN) algorithm.
D. A Naive Bayes classifier.
E. A deep learning architecture incorporating attention mechanisms and cross-modal fusion layers, with separate embedding layers for each modality, followed by a shared representation layer for joint learning and prediction.

Answer: E

Explanation:
Deep learning architectures with attention mechanisms and cross-modal fusion layers are best suited for capturing complex relationships between different modalities. Attention mechanisms allow the model to focus on the most relevant features from each modality, while cross-modal fusion layers enable joint learning and prediction based on the combined representations. Linear regression, decision trees, KNN, and Naive Bayes are less capable of capturing complex, non-linear relationships in multimodal data.

NEW QUESTION # 291
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. Increase the model's capacity by adding more layers.
B. Decrease the guidance scale during sampling.
C. Reduce the learning rate during sampling.
D. Use a larger batch size during inference.
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 # 292
Which of the following are valid techniques for dealing with overfitting in a deep learning model trained on image data?

A. Reducing the amount of training data
B. Using data augmentation techniques.
C. Adding Ll or L2 regularization.
D. Implementing dropout layers.
E. Increasing the complexity of the model.

Answer: B,C,D

Explanation:
Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. L1/L2 regularization penalizes large weights, preventing the model from becoming too complex. Data augmentation increases the Size and diversity of the training data, reducing overfitting. Dropout randomly deactivates neurons during training, preventing co-adaptation and improving generalization. Increasing model complexity or reducing training data would likely worsen overfitting.

NEW QUESTION # 293
......

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