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

2. Which of the following statements accurately describes the role of attention mechanisms in Transformer-based multimodal models?
(Select all that apply)

A) Attention mechanisms prevent vanishing gradients during training of deep neural networks.
B) Attention mechanisms are used to compress the input sequence into a fixed-length vector representation.
C) Attention mechanisms enable the model to learn relationships between different modalities, such as images and text.
D) Attention mechanisms are primarily used to reduce the computational cost of processing long sequences.
E) Attention mechanisms allow the model to focus on the most relevant parts of the input sequence when generating the output.

3. Consider a multimodal dataset containing text, images, and corresponding GPS coordinates. You want to build a model that predicts the sentiment of a social media post based on this dat a. Which of the following data preprocessing steps are crucial to ensure the model's performance and prevent data leakage?

A) Standardize the GPS coordinates (latitude and longitude) using a scaler fitted only on the training data.
B) Split the dataset into training, validation, and test sets based on time to avoid leakage of future information into the training set.
C) Resize all images to a uniform size.
D) Randomly shuffle the entire dataset before splitting it into training, validation, and test sets.
E) Normalize all text data to lowercase and remove punctuation.

4. You are tasked with optimizing a U-Net model for real-time image segmentation on an embedded device with limited GPU memory. The original model is trained in FP32 precision. Which of the following techniques, when applied together, would likely yield the best trade-off between accuracy and performance?

A) Weight clustering to reduce model size, pruning low-importance connections, and using a larger learning rate during fine-tuning.
B) FP16 mixed-precision training, layer fusion to combine multiple operations into one, and increasing the batch size to improve GPU utilization.
C) Applying standard post-training quantization to INT8, replacing convolutional layers with fully connected layers, and using a smaller batch size.
D) Converting all layers to FP16, removing skip connections from the IJ-Net architecture, and using a smaller input image resolution.
E) Quantization Aware Training (QAT) to INT8, Knowledge Distillation from the FP32 model to a smaller student model, and channel pruning to reduce the number of filters.

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) Implementing a robust input validation and filtering mechanism to remove potentially harmful or biased inputs.
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) Using only images of products from a single manufacturer for training.
E) Employing adversarial debiasing techniques during training to mitigate biases learned from the training data.

Solutions: