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NVIDIA Generative AI Multimodal Sample Questions:

1. You're building a multimodal model that takes images and text as input. You notice that your model is heavily biased towards the text modality, essentially ignoring the visual input. Which of the following strategies could you employ to address this modality imbalance? (Select TWO)

A) Increase the learning rate for the text encoder.
B) Remove the text modality entirely.
C) Use a modality-specific loss function, weighting the loss from the visual modality more heavily.
D) Reduce the size of the visual encoder.
E) Implement a gating mechanism that dynamically adjusts the contribution of each modality based on the input.

2. You are fine-tuning a pre-trained language model for a specific task. You notice that the model performs well on the training data but poorly on the validation dat a. Which of the following techniques can help mitigate this overfitting problem? (Select TWO)

A) Increase the size of the training data.
B) Apply weight decay (L2 regularization).
C) Use dropout regularization.
D) Decrease the batch size.
E) Increase the learning rate.

3. You're training a multimodal model for generating stories from images and audio. You use a Transformer architecture. During training, you notice that the model struggles to maintain long-range dependencies in the generated stories, leading to incoherent narratives. Which of the following techniques would be MOST effective in addressing this issue within the Transformer architecture?

A) Using a smaller embedding dimension.
B) Using only audio as input.
C) Incorporating positional encodings and increasing the attention window size.
D) Reducing the number of layers in the Transformer.
E) Removing the self-attention mechanism.

4. You're training a multimodal model to generate images from text prompts. The model architecture consists of a text encoder (Transformer) and an image decoder (GAN). After training, you observe that the generated images are highly realistic but often don't accurately reflect the details specified in the text prompt. What strategy would be MOST effective in improving the alignment between the text prompts and the generated images?

A) Introduce a contrastive loss that encourages the image embedding to be close to the text embedding of its corresponding prompt and far from the embeddings of other prompts.
B) Use a larger dataset of images for training the GAN.
C) Increase the capacity of the GAN's generator network.
D) Use a simpler text encoder.
E) Reduce the learning rate of the text encoder.

5. You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?

A) Decrease the learning rate.
B) Implement early stopping based on the validation loss.
C) Increase the learning rate.
D) Increase the training data size by collecting more data.
E) Increase the weight decay (L2 regularization).

Solutions: