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NVIDIA Generative AI Multimodal Sample Questions:

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

2. A multimodal AI model, designed to translate sign language videos into text, is consistently failing on specific gestures that involve rapid hand movements. Which optimization technique targeting temporal data processing would be MOST effective to apply?

A) Use a recurrent neural network (RNN) or Transformer with attention mechanisms specifically designed for handling sequential data.
B) Downsample the video frames to reduce computational complexity.
C) Apply frame interpolation techniques to increase the video's frame rate.
D) Perform image segmentation to isolate the hands in each frame.
E) Increase the batch size to improve GPU utilization during training.

3. Consider the following code snippet which aims to create a custom prompt for a Stable Diffusion model using the 'diffusers* library. The goal is to generate an image of 'a cat wearing a hat sitting on a chair'. Which of the following modifications would MOST effectively improve the quality and coherence of the generated image?

A) Increasing the 'guidance_scale' and adding a negative prompt such as 'blurry, distorted'.
B) Decreasing the number of inference steps to speed up the generation process.
C) Adding more unrelated keywords to the prompt to increase diversity.
D) Removing the word 'sitting' from the prompt to make it more general.
E) Using a smaller image resolution to reduce computational cost.

4. You're tasked with building a system that generates personalized exercise recommendations based on user's text descriptions of their fitness goals and images of their current physical condition. Due to privacy concerns, you cannot directly access the user's raw images or text after initial processing. What technique can allow you to continue to train the model while respecting these privacy constraints?.

A) Federated Learning
B) Reinforcement Learning
C) Transfer Learning
D) Generative Adversarial Networks (GANs)
E) Data Augmentation

5. You are working with a transformer-based multimodal model that processes both text and audio. You want to implement an efficient attention mechanism that reduces the computational cost associated with attending to the entire input sequence. Which of the following attention mechanisms would be MOST suitable for achieving this goal?

A) Local Attention
B) Scaled Dot-Product Attention
C) Global Attention
D) Multi-Head Attention
E) Sparse Attention

Solutions: