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NVIDIA Generative AI Multimodal Sample Questions:

1. You're training a Generative Adversarial Network (GAN) to generate images from text descriptions. After a few epochs, you notice the generator is producing nearly identical images regardless of the text input (mode collapse). Which of the following strategies could help mitigate this issue?

A) Apply weight decay regularization to the generator
B) Increase the capacity (number of layers/neurons) of the discriminator.
C) Decrease the learning rate of the discriminator.
D) Use a larger batch size for the generator.
E) Implement mini-batch discrimination in the discriminator.

2. You are experimenting with a text-to-image generative model. You notice that when prompted with descriptions containing specific demographic information (e.g., 'a black doctor'), the generated images consistently reflect stereotypes. What steps can you take during the experiment evaluation phase to identify and mitigate this bias? (Select TWO)

A) Use a bias detection metric to quantify the presence of bias in the generated images, comparing output distributions across different demographic groups.
B) Randomly shuffle the training dataset to minimize bias.
C) Increase the size of the training dataset to dilute the effect of any biased examples.
D) Conduct a human evaluation study where participants assess the generated images for stereotypical representations.
E) Filter out all examples containing demographic information from the training dataset.

3. You are building a multimodal generative A1 application that uses CLIP to align text and image embeddings. You observe that the generated images lack detail and fidelity to the text prompt. Which of the following strategies would be MOST effective in improving image quality, and how could prompt engineering and Triton Inference Server play a role?

A) Using a larger batch size during CLIP training and increasing the learning rate. Triton is not directly involved in model training.
B) Increasing the CLIP model's text encoder's hidden layer size and using more aggressive data augmentation during CLIP training. Triton can be used to serve the augmented CLIP model at scale.
C) Refining the text prompts to be more descriptive and specific, incorporating stylistic details and relevant keywords. Triton can optimize the prompt embedding process.
D) All of the above
E) Training a separate image super-resolution model to enhance the generated images after they are produced by the CLIP-guided generator. Triton can manage the concurrent execution of the generator and super-resolution models.

4. When experimenting with different architectures for a text-to-image model, you observe that a Diffusion model generates higher quality images than a GAN (Generative Adversarial Network). However, the Diffusion model is significantly slower to generate images. What strategy can you employ to improve the inference speed of the Diffusion model without significantly sacrificing image quality?

A) Use a larger UNet architecture within the Diffusion model.
B) Train the GAN for a longer duration.
C) Increase the number of diffusion steps.
D) Use a smaller batch size.
E) Employ distillation techniques to train a faster, smaller model.

5. Consider the following code snippet using a hypothetical Generative A1 library. This code is intended to generate an image from a text prompt and then refine it based on a user-provided style image. However, it's not producing the desired results. What is the MOST likely cause of the issue?

A) The text prompt provided is too short.
B) The 'generate_image' function does not support the parameter.
C) The 'strength' parameter in 'refine_image' is set too low, resulting in minimal stylistic changes.
D) The library being used is incompatible with the GPU.
E) The 'style_image' is not preprocessed correctly before being passed to the 'refine_image' function.

Solutions: