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NVIDIA Generative AI Multimodal Sample Questions:

1. Which of the following statements are TRUE regarding the challenges of training multimodal machine learning models? (Select TWO)

A) Aligning data from different modalities with varying temporal resolutions (e.g., high-frame-rate video and low-frequency audio) is a significant challenge.
B) Multimodal models are immune to the problem of overfitting due to the diverse nature of the input data.
C) All available open-source tools readily support multimodal architectures and loss functions, so there are no software-related challenges.
D) Multimodal models are generally easier to train than unimodal models due to the increased information available.
E) Handling missing modality data (e.g., missing image for a text input) requires specialized techniques.

2. You are optimizing a multimodal model for deployment on an edge device with limited memory and computational resources. The model takes video frames and audio as input to predict human actions. Which of the following optimization techniques would provide the MOST significant reduction in model size and computational complexity without drastically sacrificing accuracy?

A) Increasing the number of layers in the neural network.
B) Increasing the sampling rate of the audio input.
C) Using larger kernel sizes in convolutional layers.
D) Applying knowledge distillation to train a smaller student model.
E) Removing batch normalization layers.

3. You are working with a multimodal dataset containing medical images (X-rays) and corresponding patient reports (text). Some of the reports are missing or incomplete. Which of the following strategies would be most appropriate to handle this missing data in a multimodal AI model?

A) Using a simple average of all available reports for imputation.
B) Training the model only on the complete data points and ignoring the incomplete ones.
C) Discarding all data points with missing reports.
D) Using a multimodal autoencoder to reconstruct the missing reports from the available image data, or using a masked language model to predict missing words in the existing reports, conditioned on the image.
E) Imputing the missing reports with a generic placeholder text.

4. You are building a real-time image captioning system using a Transformer model. You observe significant latency issues when generating captions for high-resolution images. Which optimization strategies would be most effective in reducing the latency without significantly sacrificing caption quality? (Select all that apply)

A) Employ model quantization techniques (e.g., INT8 quantization) to reduce model size and memory bandwidth requirements.
B) Use mixed precision training and inference, leveraging Tensor Cores on NVIDIA GPUs.
C) Increase the number of Transformer layers to improve caption accuracy, even if it increases latency.
D) Decrease the batch size during inference.
E) Implement knowledge distillation, training a smaller, faster student model to mimic the behavior of the larger Transformer model.

5. You're tasked with building a model that can generate recipes from images of food. You decide to use a Variational Autoencoder (VAE) architecture. What would be a suitable loss function combination for this task, considering both reconstruction accuracy and recipe relevance?

A) KL divergence loss only-
B) Reconstruction loss (MSE) + KL divergence loss + Cross-entropy loss between the generated recipe and a plausible recipe given the generated image embedding-
C) Reconstruction loss (MSE) + Perceptual loss (based on a pre-trained image classifier) only
D) KL divergence loss + Cosine Similarity loss between the generated image embedding and a text embedding of a random recipe.
E) Reconstruction loss (MSE) between the input image and the decoded image only

Solutions: