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NVIDIA Generative AI Multimodal Sample Questions:

1. You're developing a multimodal system that takes an image and a short audio clip as input and generates a relevant story. You've trained the model, but you observe that the generated stories tend to heavily favor the content of the audio clip, largely ignoring the image. Which of the following techniques could you employ to better balance the influence of both modalities?

A) Reduce the dimensionality of the audio embedding_
B) Implement modality-specific scaling factors or attention mechanisms to dynamically adjust the contribution of each modality during the fusion process.
C) Increase the learning rate of the image encoder.
D) Increase the size of the training dataset, ensuring it contains more diverse audio clips.
E) Add more layers to the audio encoder.

2. You are tasked with visualizing the performance of a Generative A1 model across different categories of input dat a. You need to show both the accuracy and the number of data points in each category. Which visualization technique would be MOST effective for this purpose?

A) A scatter plot showing the relationship between accuracy and sample size for each category.
B) A bar chart showing the accuracy for each category, with error bars indicating the sample size.
C) A combination chart (e.g., bar and line) with bars showing the accuracy and a line showing the sample size.
D) A table showing the accuracy and sample size for each category.
E) A pie chart showing the accuracy for each category.

3. Which of the following techniques can be used to reduce the computational cost and memory footprint of large language models (LLMs) during inference?

A) Adding more layers
B) Quantization
C) Knowledge Distillation
D) Pruning
E) Increasing the model size

4. You're tasked with building a system that can generate realistic images from text descriptions and, conversely, generate accurate text descriptions from images. You decide to use a GAN (Generative Adversarial Network) architecture, but need to handle both modalities effectively. What GAN variant would be MOST suitable for this bi-directional multimodal task?

A) Vanilla GAN
B) Super-Resolution GAN (SRGAN)
C) CycleGAN
D) Conditional GAN (cGAN)
E) Deep Convolutional GAN (DCGAN)

5. You are working on a project to generate realistic images from text descriptions. You've trained a diffusion model, but the generated images often lack fine-grained details and exhibit artifacts. Which of the following techniques would be MOST effective in improving the image quality and fidelity?

A) Use a larger batch size during training.
B) Reduce the learning rate during training.
C) Implement classifier-free guidance, adjusting the guidance scale to balance fidelity and diversity.
D) None of the above.
E) Increase the number of diffusion steps during image generation.

Solutions: