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NVIDIA Generative AI Multimodal Sample Questions:

1. You are developing a text-to-image generation system using a diffusion model. During inference, you notice that the generated images often contain artifacts or inconsistencies. What is the most appropriate strategy to reduce these artifacts and improve the overall image quality?

A) Increase the number of diffusion steps during the reverse process (sampling).
B) Use a simpler text encoder to reduce noise in the conditioning signal.
C) Reduce the batch size during inference.
D) Train the model with a larger dataset of higher-resolution images.
E) Decrease the guidance scale (classifier-free guidance).

2. You're building a real-time voice cloning application using NVIDIA Riv
a. You need to ensure high-quality synthesized speech with minimal latency. Which of the following Riva configurations would provide the BEST trade-off between quality and speed?

A) Using a pre-trained, open-source text-to-speech model and a CPU-based vocoder, optimized for minimal memory footprint.
B) Using a large, high-capacity Tacotron 2 text-to-speech model and a high-resolution WaveGlow vocoder, deployed on a single, low-power GPU.
C) Using only the open source implementation and not NVIDIA Riva to implement a Voice Cloning application
D) Using a smaller, faster FastSpeech text-to-speech model and a parallel WaveGAN vocoder, deployed on a multi-GPU server with TensorRT optimization enabled.
E) Using a large, transformer-based text-to-speech model with aggressive quantization and pruning, deployed on a cloud-based TPIJ instance.

3. During the training of a multimodal Generative A1 model, you observe that the gradients are vanishing, leading to slow convergence.
Which of the following techniques can help mitigate the vanishing gradient problem?

A) Using ReLlJ or Leaky ReLIJ activation functions.
B) Using Batch Normalization.
C) Employing skip connections (e.g., ResNet blocks).
D) Applying gradient clipping.
E) Using Sigmoid activation functions.

4. You are building a multimodal generative A1 system that creates 3D models from text descriptions. The system produces accurate shapes but struggles to generate realistic textures and surface details. What approach would BEST address this limitation?

A) Add more layers to the shape decoder.
B) Reduce the resolution of the generated 3D models to simplify the texture generation process.
C) Increase the batch size during the 3D model generation phase.
D) Increase the number of parameters in the text encoder.
E) Train a separate texture generation network conditioned on the generated 3D shape.

5. You are fine-tuning a pre-trained large language model (LLM) for a specific text generation task. During training, you observe that the model is overfitting to the training data and not generalizing well to unseen examples. Which of the following techniques could be MOST effective in mitigating overfitting in this scenario?

A) Applying dropout regularization to the LLM's layers.
B) Increasing the size of the training dataset.
C) Using a smaller batch size during fine-tuning.
D) Early stopping based on a validation set.
E) Decreasing the learning rate during fine-tuning.

Solutions: