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NVIDIA Generative AI Multimodal Sample Questions:

1. You have trained a text-to-image diffusion model. During inference, you notice that the generated images often lack fine-grained details and appear blurry. Which of the following techniques could you apply to improve the image quality without retraining the model?

A) Decrease the guidance scale during sampling.
B) Increase the model's capacity by adding more layers.
C) Use a larger batch size during inference.
D) Reduce the learning rate during sampling.
E) Increase the number of diffusion steps during sampling.

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

3. You are building a text-to-image generation pipeline using CLIP and a diffusion model. After training, you notice that the generated images often lack the specific details mentioned in the text prompts. Which of the following strategies could you employ to improve the alignment between text and image?

A) Increase the number of diffusion steps during the image generation process.
B) Fine-tune the CLIP model on a dataset of text-image pairs relevant to your desired domain.
C) All of the above.
D) Use negative prompt engineering to guide the diffusion process away from undesired attributes.
E) Increase the number of layers in the I-I-Net architecture of the diffusion model.

4. You are training a multimodal model to predict stock prices using news articles (text) and historical price charts (images). You notice the model is overfitting to the historical price charts and largely ignoring the news articles. What is a potential solution to mitigate this overfitting?

A) Increase the learning rate for the image processing component of the model.
B) Apply stronger regularization (e.g., dropout, Ll/L2 regularization) to the image processing component and/or increase the weight of the text-based loss function.
C) Use a simpler model architecture for processing text.
D) Reduce the batch size for the text data.
E) Remove the image data entirely to prevent overfitting.

5. You are deploying a text-to-speech application using NVIDIA Riv
a. The application needs to handle a large volume of concurrent requests with minimal latency. Which of the following Riva deployment configurations would be MOST appropriate?

A) Deploying Riva on CPU using multiprocessing.
B) Deploying Riva across multiple GPUs using Triton Inference Server with dynamic batching.
C) Deploying Riva on a single CPU core with a small batch size.
D) Deploying Riva on a single GPU with a large batch size.
E) Deploying Riva on a single GPU using TensorRT for model optimization, without using Triton Inference Server.

Solutions: