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NVIDIA Generative AI Multimodal Sample Questions:

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

2. You are building a multimodal model for medical image diagnosis, using both radiology images (e.g., X-rays) and patient clinical notes.
The clinical notes are highly unstructured and contain significant medical jargon. What preprocessing steps would be MOST effective for improving the model's performance?

A) Translating the clinical notes into multiple languages and then back-translating to the original language.
B) Directly feeding the raw clinical notes into the model without any preprocessing.
C) Performing sentiment analysis on the clinical notes.
D) Utilizing named entity recognition (NER) to identify medical entities (diseases, medications, etc.), and employing a medical-specific language model (e.g., BioBERT) for text embeddings.
E) Applying basic text cleaning (removing punctuation, converting to lowercase) and using a standard word embedding (e.g., Word2Vec).

3. You are working with a pre-trained multimodal model that takes images and text as input. You want to fine-tune this model for a specific downstream task, but you have limited computational resources. Which of the following techniques would be most effective for reducing the memory footprint and computational cost during fine-tuning?

A) Freezing all layers of the pre-trained model and training only a small classification head.
B) Increasing the batch size to utilize the available memory more efficiently.
C) Applying knowledge distillation, where a smaller student model is trained to mimic the behavior of the pre-trained model.
D) Fine-tuning the entire model with a small learning rate.
E) Using quantization to reduce the precision of the model's weights and activations.

4. 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 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 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 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.

5. You are training a multimodal Generative A1 model for generating video captions. The model is overfitting to the training data, resulting in poor generalization to unseen videos. Which of the following regularization techniques would be MOST suitable to mitigate overfitting?

A) Batch normalization
B) Early stopping based on a validation set
C) Data augmentation (e.g., adding noise to the video frames)
D) Dropout
E) L1 regularization on the model weights

Solutions: