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NVIDIA Generative AI Multimodal Sample Questions:

1. Consider a multimodal emotion recognition system that uses both facial expressions (images) and speech (audio). You want to fuse the information from these two modalities at the decision level. Which of the following techniques would be MOST suitable for decision-level fusion?

A) Train separate classifiers for images and audio, then use the output of the image classifier as input to the audio classifier-
B) Concatenate the feature vectors extracted from the images and audio, then train a single classifier.
C) Train separate classifiers for images and audio, then use a weighted average of their output probabilities based on the confidence scores of each classifier.
D) Train a single transformer to process both images and audio in sequence.
E) Train separate classifiers for images and audio, then average their output probabilities for each emotion class.

2. You're building a text generation model using a Transformer architecture. You observe that the generated text often gets stuck in repetitive loops, producing the same phrase over and over. Which of the following strategies is MOST likely to mitigate this issue?

A) Decrease the learning rate of the model during training.
B) Increase the number of attention heads in the Transformer.
C) Increase the temperature parameter during text generation.
D) Implement beam search with a larger beam width.
E) Use a smaller vccabulary size.

3. You are tasked with evaluating a multimodal A1 model that combines image and text inputs to generate product descriptions. You observe that the model performs well on common product categories (e.g., clothing, electronics) but struggles with niche categories (e.g., antique furniture, scientific instruments). Which of the following strategies would be MOST effective in improving the model's performance on niche categories?

A) Increase the overall size of the training dataset.
B) Implement data augmentation techniques to create synthetic data for niche categories.
C) Fine-tune the model on a dataset specifically curated for niche product categories.
D) Replace the image encoder with a more powerful architecture.
E) Decrease the learning rate during training.

4. You are developing a multimodal system for medical diagnosis using MRI images and patient history text. Your initial model performs poorly on patients with rare conditions. Which of the following data augmentation techniques would be MOST effective in improving the model's performance on these under-represented cases?

A) Using a Generative Adversarial Network (GAN) to synthesize new MRI images conditioned on the patient history text, specifically targeting the rare conditions-
B) Back-translating the patient history text to generate paraphrased versions-
C) Randomly cropping and rotating MRI images.
D) Replacing named entities in the patient history text with other random entities-
E) Adding Gaussian noise to the pixel values of MRI images.

5. Which of the following are valid techniques for improving the trustworthiness of a Generative A1 model used for generating product descriptions based on images, specifically addressing potential biases and ensuring fairness? (Select TWO)

A) Employing adversarial debiasing techniques during training to mitigate biases learned from the training data.
B) Actively curating a diverse and representative training dataset that reflects the real-world distribution of products and avoiding over-representation of specific categories.
C) Ignoring fairness metrics during model evaluation to focus solely on overall accuracy.
D) Implementing a robust input validation and filtering mechanism to remove potentially harmful or biased inputs.
E) Using only images of products from a single manufacturer for training.

Solutions: