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NVIDIA Generative AI Multimodal Sample Questions:

1. You are evaluating a multimodal model that generates descriptions for video clips. You have human ratings for the relevance, fluency, and coherence of the generated descriptions. Which statistical test is MOST appropriate for determining if there is a statistically significant difference in the median ratings for each of these criteria (relevance, fluency, coherence) between two different versions of your model?

A) ANOVA
B) Kruskal-Wallis test
C) T-test
D) Friedman Test
E) Mann-Whitney U test

2. You are building a multimodal RAG application that integrates text documents and images. You've noticed that when a user query relates strongly to the visual content, the retrieved documents are less relevant than desired. Which of the following strategies would MOST effectively improve the retrieval of relevant information in this scenario?

A) Use a larger language model for the generative component of the RAG pipeline.
B) Implement cross-modal embedding, training a model to create a joint embedding space for text and images.
C) Prepend the image captions to all the source documents to enhance text-based retrieval.
D) Increase the k value (number of retrieved documents) in the vector search.
E) Fine-tune the existing text embedding model with more text data.

3. You are training a deep convolutional generative adversarial network (DCGAN) for generating high-resolution images. After several epochs, you observe mode collapse the generator produces only a few similar images. Which of the following strategies would be most effective in mitigating mode collapse?

A) Decrease the learning rate of the generator and discriminator simultaneously.
B) Increase the batch size significantly to provide the discriminator with a more diverse set of samples.
C) Use label smoothing in the discriminator to penalize overconfident predictions.
D) Introduce batch normalization only in the generator network.
E) Implement feature matching in the discriminator by making the generator learn to match intermediate layer activations of the discriminator on real data.

4. You are building a system that uses both video and text to determine the sentiment of movie reviews. You notice that while your system works great on the training set, the performance is much worse on the validation set. What is the MOST likely reason for this and what methods can you use to improve the performance?

A) The text data is corrupt. Clean the text data by ensuring that the text is not noisy or missing.
B) The training data is not representative enough of the real world. Gather new data that matches the real world, or introduce a cross validation training routine.
C) The model is not complex enough. Use a larger model or different model to improve results.
D) The model is overfitting on the training data. Use regularization techniques or more training data to overcome this.
E) The Video Data is too Large. Consider compressing the video data to ensure that it all fits into memory.

5. You are tasked with building a multimodal generative AI model to create marketing content from product images and descriptions. The image encoder uses a pre-trained ResNet50 model, and the text encoder uses a pre-trained BERT model. After initial training, the generated content frequently misinterprets the image. Which of the following strategies is MOST effective in improving the model's ability to correctly interpret the image within the multimodal context?

A) Freeze the weights of both the ResNet50 and BERT models to prevent overfitting.
B) Replace ResNet50 with a simpler image encoder like a shallow CNN to reduce computational complexity.
C) Fine-tune the ResNet50 model with a dataset of images specifically related to the product domain, using a contrastive loss function that encourages representations of images and corresponding text to be close in the embedding space.
D) Decrease the batch size during training.
E) Increase the learning rate for the BERT model to prioritize text-based information.

Solutions: