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NVIDIA Generative AI Multimodal Sample Questions:

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

2. You are building a system that generates image captions from images and vice vers a. Which evaluation metric(s) are MOST appropriate to assess the quality of the generated content? (Select all that apply)

A) FID (Frechet Inception Distance)
B) Inception Score
C) ROUGE score
D) Accuracy
E) BLEU score

3. You're building a multimodal model that integrates text, images, and audio. The text data has many missing values. Which of the following strategies would be MOST effective for handling missing text data while leveraging the other modalities?

A) Use a multimodal generative model (e.g., VAE, GAN) to impute the missing text based on the learned joint representation of all modalities.
B) Train a separate model to predict the missing text based on the available image and audio data, then impute the predicted values.
C) Ignore the missing text values during training, assuming the model can learn from the available modalities.
D) Use a simple imputation method like replacing missing text with a placeholder like 'unknown'.
E) Remove all data points with missing text values to ensure data integrity.

4. You're working with a text-to-image generation model. After training, you notice the generated images lack fine-grained details and appear blurry. Which hyperparameter tuning strategy would be MOST effective in improving the visual quality of the generated images, considering the computational cost?

A) Adding more layers to the discriminator network (if using GANs).
B) Switching to a different model architecture (e.g., from VAE to GAN).
C) Increasing the number of training epochs.
D) Decreasing the batch size.
E) Optimizing the learning rate schedule.

5. You are building a multimodal generative model that combines text and images. The goal is to generate realistic images based on textual descriptions. You have access to a pre-trained language model (e.g., BERT) and a pre-trained image generation model (e.g., StyleGAN). Which of the following architectures would be MOST suitable for effectively integrating these two models to achieve your objective?

A) Using the language model to generate a latent vector that is then fed into the image generation model as input.
B) Fine-tuning the language model to directly output pixel values for the image.
C) Concatenating the text and image data into a single vector and feeding it into a standard feedforward neural network.
D) Training a separate neural network to map the image to the text description.
E) Using the language model to generate captions for the images, and then training the image generation model on the captions.

Solutions: