NVIDIA Generative AI Multimodal Sample Questions:

1. You're building a system to translate speech to text using an encoder-decoder architecture with attention. You observe that the translated text often repeats phrases from the input speech. Which regularization techniques could help mitigate this issue? (Select TWO)

A) Applying label smoothing to the target sequences.
B) Decreasing the number of attention heads.
C) Adding dropout to the encoder and decoder layers.
D) Adding L1 regularization to the embedding layer.
E) Increasing the size of the vocabulary.

2. When deploying a multimodal Generative A1 model for a real-time application, such as a virtual assistant that responds to voice commands and displays relevant images, which of the following considerations are MOST critical for ensuring low latency and a smooth user experience? (Select TWO)

A) Employ model quantization and pruning techniques to reduce model size and computational requirements.
B) Disable any logging or monitoring to reduce overhead.
C) Prioritize model accuracy over inference speed.
D) Deploy the model on a single CPU core to minimize resource contention.
E) Utilize asynchronous processing and caching strategies to pre-compute and store frequently accessed data.

3. Consider a scenario where you are developing a multimodal system for generating 3D models from text descriptions. The system uses a Variational Autoencoder (VAE) to generate the 3D models. During training, you observe that the generated 3D models lack diversity and tend to cluster around a few common shapes. Which of the following techniques could you employ to improve the diversity of the generated 3D models?

A) Increasing the weight of the Kullback-Leibler (KL) divergence term in the VAE's loss function.
B) Decreasing the batch size during training.
C) Using a larger training dataset with more diverse text descriptions.
D) Decreasing the capacity of the VAE's latent space.
E) Applying techniques like adversarial training to encourage the VAE to generate more realistic 3D models.

4. You are building a multimodal model that combines text and image data to generate captions. The text encoder is a pre-trained BERT model, and the image encoder is a ResNet-50. You observe that the generated captions are heavily biased towards descriptions based on the text input, and the image information is not well represented. Which of the following techniques could you apply to improve the contribution of the image modality?

A) Freeze the weights of the ResNet-50 image encoder
B) Increase batch size substantially.
C) Decrease the dimensionality of the image embeddings using PCA
D) Increase the learning rate of the BERT text encoder
E) Apply a modality-specific loss weight, giving higher weight to the image loss during training-

5. You're training a VQA (Visual Question Answering) model. During evaluation, you notice the model performs well on common object recognition tasks but struggles with questions requiring reasoning about object relationships or scene understanding. What are the MOST effective strategies to improve the model's performance on these complex reasoning tasks? (Choose two)

A) Increase the size of the image embedding.
B) Use a more sophisticated attention mechanism that attends to relevant image regions based on the question.
C) Decrease the learning rate.
D) Train the model on a larger dataset with more diverse and complex questions/answers.
E) Replace the LSTM with a simpler RNN in the question encoder.

Solutions: