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NVIDIA Generative AI Multimodal Sample Questions:

1. You are training a multimodal model that combines text and images. You observe that the model is heavily biased towards the text modality and largely ignores the image data. Which of the following strategies could you use to address this modality imbalance? (Select all that apply)

A) Oversample the image data during training.
B) Reduce the dimensionality of the image features to match the dimensionality of the text embeddings.
C) Use a modality-specific loss weighting scheme, assigning a higher weight to the loss component derived from the image data.
D) Decrease the learning rate for the text-related parameters of the model.
E) Increase the learning rate for the image-related parameters of the model.

2. 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 TF-IDF for text, pixel values for images, and one-hot encoding for user profile information.
B) 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.
C) 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.
D) Using character-level n-grams for text, edge detection for images, and boole an features for user profile information.
E) 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.

3. Consider a multimodal dataset containing patient records: text descriptions of symptoms, MRI images, and audio recordings of heart sounds. Some records are missing MRI images. Which of the following methods is BEST suited for handling this missing data within a multimodal learning framework?

A) Using a masking approach during training, where the model is trained to predict the missing modality (MRI) from the available modalities (text and audio) for incomplete records and is trained with all modalities for complete records.
B) Imputing missing MRI images using the average MRI image from the entire dataset.
C) Deleting all records with missing MRI images.
D) Training a separate model only on records with complete data and then using it to predict the missing data.
E) Ignoring the MRI data completely and training the model only on the text and audio data.

4. When experimenting with different architectures for a text-to-image model, you observe that a Diffusion model generates higher quality images than a GAN (Generative Adversarial Network). However, the Diffusion model is significantly slower to generate images. What strategy can you employ to improve the inference speed of the Diffusion model without significantly sacrificing image quality?

A) Employ distillation techniques to train a faster, smaller model.
B) Train the GAN for a longer duration.
C) Increase the number of diffusion steps.
D) Use a larger UNet architecture within the Diffusion model.
E) Use a smaller batch size.

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

Solutions: