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NVIDIA Generative AI Multimodal Sample Questions:

1. You are using NeMo to fine-tune a large language model for a specific task. You notice that the model is overfitting to the training dat a. Which of the following techniques could you apply to mitigate overfitting in this scenario? (Select all that apply)

A) Decrease the learning rate.
B) Add dropout layers to the model architecture.
C) Increase the size of the training dataset.
D) Increase the batch size.
E) Implement weight decay (L2 regularization).

2. You've trained a large multimodal model that takes text and images as input and generates creative stories. While the model produces high-quality stories in general, it occasionally generates outputs that are factually incorrect or nonsensical. Which of the following techniques would be MOST effective in improving the model's factual accuracy and coherence?

A) Removing dropout layers.
B) Increasing the model size by adding more layers.
C) Implementing a retrieval-augmented generation (RAG) approach.
D) Reducing the temperature parameter during generation.
E) Training the model on a smaller dataset.

3. You are training a text-to-image diffusion model and observe that the generated images often exhibit a 'washed-out' or overly smooth appearance. Which of the following adjustments to the training process would likely improve the image quality and detail?

A) Increase the weight of the perceptual loss function in the training objective.
B) Decrease the number of diffusion steps used during training.
C) Reduce the learning rate for the U-Net architecture within the diffusion model.
D) Reduce the batch size used during training to minimize memory consumption.
E) Apply more aggressive data augmentation techniques to the training dataset.

4. Consider you are working on a project that aims at generating photorealistic images from segmentation maps, using a conditional GAN architecture. The training process is unstable, frequently exhibiting mode collapse and artifacts. Describe a series of techniques, ranked by their likely impact, to mitigate these issues.

A) 1. Reduce the number of layers in the discriminator. 2. Increase the learning rate of the generator. 3. Disable batch normalization.
B) 1. None of the above
C) 1. Increase batch size. 2. Decrease learning rate. 3. Add more convolutional layers.
D) 1. Implement Spectral Normalization. 2. Use PatchGAN discriminator. 3. Apply data augmentation (e.g., random flips, jitter).
E) 1. Switch to a Transformer-based architecture. 2. Use a larger dataset. 3. Decrease the number of channels in the generator.

5. You are building a multimodal emotion recognition system that combines facial expressions (images) and spoken language (audio). The image data is preprocessed using a CNN, and the audio data is processed using an LSTM. Which of the following fusion strategies would be MOST effective for combining these two modalities to predict the emotion?

A) Intermediate fusion by concatenating the CNN and LSTM hidden state representations before feeding them into a shared classification layer.
B) Using an attention mechanism to weigh the contributions of the CNN and LSTM features based on their relevance to the predicted emotion.
C) Late fusion by training separate classifiers on the CNN and LSTM outputs and then averaging their predicted probabilities.
D) Early fusion by concatenating the raw pixel values of the images with the raw audio waveform.
E) Training the CNN and LSTM models independently without any fusion.

Solutions: