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NVIDIA Generative AI Multimodal Sample Questions:

1. Consider this PyTorch code snippet related to processing multimodal dat a. What is the primary purpose of the following code in the context of Generative A1?

A) To create a custom dataset class for handling paired image and text data.
B) To resize all images to the same dimension.
C) To ensure images and text are processed in the same order during training.
D) To create separate data loaders for images and text.
E) To concatenate image and text data into a single tensor.

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

3. You are training a Variational Autoencoder (VAE) and notice that the generated samples are blurry and lack detail. Which of the following adjustments could help improve the quality and sharpness of the generated images2 Select all that apply.

A) Increase the capacity of the encoder and decoder networks by adding more layers or units.
B) Use a more powerful decoder architecture, such as one with deconvolutional layers.
C) Decrease the batch size to reduce computational complexity
D) Decrease the weight of the Kullback-Leibler (KL) divergence term in the loss function-
E) Increase the dimensionality of the latent space

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

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

5. You are building a multimodal generative A1 system that creates 3D models from text descriptions. The system produces accurate shapes but struggles to generate realistic textures and surface details. What approach would BEST address this limitation?

A) Increase the number of parameters in the text encoder.
B) Reduce the resolution of the generated 3D models to simplify the texture generation process.
C) Add more layers to the shape decoder.
D) Train a separate texture generation network conditioned on the generated 3D shape.
E) Increase the batch size during the 3D model generation phase.

Solutions: