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NVIDIA Generative AI Multimodal Sample Questions:

1. 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) Removing the word 'sitting' from the prompt to make it more general.
D) Adding more unrelated keywords to the prompt to increase diversity.
E) Using a smaller image resolution to reduce computational cost.

2. Explain the role of Tensor Cores and mixed-precision training (e.g., using FP16 or bfloat16) in accelerating the training of large generative AI models.

A) Tensor Cores are only useful for inference, not training.
B) A and B.
C) Tensor Cores perform specialized matrix multiplications optimized for lower-precision data types, enabling faster computation and reduced memory footprint.
D) Mixed-precision training allows using lower precision for forward and backward passes but keeps weights and gradients in higher precision to maintain stability.
E) Mixed-precision training guarantees the same convergence behavior as full-precision training.

3. Consider the following code snippet using a hypothetical Generative A1 library. This code is intended to generate an image from a text prompt and then refine it based on a user-provided style image. However, it's not producing the desired results. What is the MOST likely cause of the issue?

A) The 'style_image' is not preprocessed correctly before being passed to the 'refine_image' function.
B) The 'generate_image' function does not support the parameter.
C) The text prompt provided is too short.
D) The library being used is incompatible with the GPU.
E) The 'strength' parameter in 'refine_image' is set too low, resulting in minimal stylistic changes.

4. You are building a multimodal generative model that combines text and images. The goal is to generate realistic images based on textual descriptions. You have access to a pre-trained language model (e.g., BERT) and a pre-trained image generation model (e.g., StyleGAN). Which of the following architectures would be MOST suitable for effectively integrating these two models to achieve your objective?

A) Concatenating the text and image data into a single vector and feeding it into a standard feedforward neural network.
B) Using the language model to generate a latent vector that is then fed into the image generation model as input.
C) Using the language model to generate captions for the images, and then training the image generation model on the captions.
D) Fine-tuning the language model to directly output pixel values for the image.
E) Training a separate neural network to map the image to the text description.

5. You are tasked with creating a multimodal AI application that analyzes social media posts containing text, images, and user profile information to predict the likelihood of a post going viral. Which feature engineering techniques are most effective for representing and integrating these different modalities?

A) Using character-level n-grams for text, edge detection for images, and boole an features for user profile information.
B) Using TF-IDF for text, pixel values for images, and one-hot encoding for user profile information.
C) Using word embeddings (e.g., Word2Vec, GloVe) for text, pre-trained CNN features (e.g., from ResNet, Inception) for images, and embedding user profiles using a graph embedding technique.
D) Using bag-of-words for text, histogram of oriented gradients (HOG) for images, and simple numerical features (e.g., number of followers) for user profiles.
E) Using a combination of TF-IDF for text, pixel values for images, and numerical features for user profile information. Then apply PCA for dimensionality reduction.

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