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NVIDIA Generative AI Multimodal Sample Questions:

1. You're building a text generation model using a Transformer architecture. You observe that the generated text often gets stuck in repetitive loops, producing the same phrase over and over. Which of the following strategies is MOST likely to mitigate this issue?

A) Use a smaller vccabulary size.
B) Increase the temperature parameter during text generation.
C) Increase the number of attention heads in the Transformer.
D) Implement beam search with a larger beam width.
E) Decrease the learning rate of the model during training.

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

3. You are building a multimodal emotion recognition system that takes both facial expressions (images) and speech audio as input. During development, you observe that the model is heavily biased towards the audio modality, effectively ignoring the visual input. Which technique would be the LEAST effective in mitigating this modality bias?

A) Reweighting the loss function to penalize errors made based on the less dominant modality (image).
B) Increasing the complexity of the audio processing branch and simplifying the image processing branch of the model.
C) Adversarial training to make each modality indistinguishable.
D) Modality dropout: Randomly dropping out one of the modalities during training.
E) Gradient blending: Adjusting the gradients from each modality based on their relative importance.

4. You are developing a generative A1 model for medical image segmentation using U-Net architecture. The input images are high- resolution MRI scans. Which of the following techniques would be MOST effective in mitigating the vanishing gradient problem during training, considering memory constraints on your GPU?

A) Using a smaller batch size and increasing the number of training epochs.
B) Employing gradient clipping and using Leaky ReLU or ELU activation functions.
C) Implementing skip connections within the U-Net architecture and using ReLU activation functions.
D) Increasing the depth of the U-Net and using sigmoid activation functions.
E) Replacing standard convolutional layers with transposed convolutional layers throughout the network.

5. You're building a system that generates images from text descriptions, incorporating spatial relationships. For instance, the text 'A red ball is to the left of a blue cube' should result in an image where the red ball is actually positioned to the left of the blue cube. Which of the following approaches would be MOST suitable for encoding and utilizing spatial information in this text-to-image generation process?

A) Relying solely on the image decoder to learn spatial relationships implicitly from the text description during training.
B) Augmenting the text encoder with explicit spatial relation embeddings that represent the relative positions between objects. Use these embeddings to modulate the image generation process (e.g., through attention mechanisms).
C) Applying a pre-trained object detector to the generated image and penalizing the model if the spatial relationships are incorrect
D) Using a standard Transformer architecture for text encoding without any specific spatial awareness mechanisms
E) Using a bag-of-words representation for the text, ignoring word order and spatial relationships.

Solutions: