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NVIDIA Generative AI Multimodal Sample Questions:

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

2. You are tasked with optimizing a multimodal A1 model that processes both image and text data for generating image captions. The model exhibits slow inference times, particularly when handling high-resolution images. Which of the following optimization strategies would be MOST effective in reducing inference latency, considering the NVIDIA ecosystem?

A) Switching to a larger model architecture with more parameters.
B) Implementing TensorRT for model optimization and quantization.
C) Using a simpler loss function during training.
D) Increasing the batch size during inference to better utilize GPU resources.
E) Removing dropout layers from the model.

3. You're working with a multimodal model that fuses text and image features. You've noticed that the model performs poorly when the text and image are semantically misaligned (e.g., an image of a dog and the caption 'a cat on a mat'). Which of the following techniques can help improve the model's robustness to such misalignment?

A) Decreasing the batch size.
B) Removing dropout layers from the model architecture.
C) Using only positive text-image pairs for training.
D) Adding a contrastive loss that penalizes embeddings of misaligned text-image pairs.
E) Increasing the learning rate during training.

4. You are tasked with building a Generative A1 model to generate realistic images of outdoor scenes. The training dataset contains a large number of images with varying lighting conditions, weather conditions, and object compositions. Which data augmentation techniques would be MOST effective in improving the model's robustness and generalization ability?

A) Applying a fixed rotation of 90 degrees to all images.
B) Random cropping and resizing.
C) Color jittering (brightness, contrast, saturation, hue), adding Gaussian noise, and random perspective transformations.
D) Horizontal flipping only.
E) Vertical flipping only

5. You're building a system that uses a pre-trained large language model (LLM) for generating creative stories. After deploying the system, you notice that the generated stories often contain biases present in the training data of the LLM. What are the MOST effective strategies to mitigate these biases in your generated stories? (Select TWO)

A) Increase the temperature parameter in the LLM's decoding strategy.
B) Reduce the size of the LLM to minimize memory usage.
C) Apply bias detection and mitigation techniques to the LLM's output.
D) Fine-tune the LLM on a diverse and representative dataset.
E) Use prompt engineering to steer the LLM away from biased outputs.

Solutions: