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NVIDIA Generative AI Multimodal Sample Questions:

1. When deploying a large multimodal model to a resource-constrained environment (e.g., an edge device), which optimization techniques are MOST crucial to consider? (Select all that apply)

A) Model quantization to reduce the model's memory footprint and computational requirements.
B) Pruning to remove less important connections from the model.
C) Increasing the batch size to improve throughput.
D) Adding more layers to the model to improve accuracy.
E) Knowledge distillation to transfer knowledge from a larger, more accurate model to a smaller, faster model.

2. You are working on a Generative A1 Multimodal model that takes text and audio as input and generates a video. During training, you observe that the generated videos often lack coherence with the input text. What are the potential issues you would investigate? (Select THREE)

A) The discriminator network is too powerful, leading to mode collapse.
B) The training dataset does not contain enough diverse examples of text, audio, and video combinations.
C) Lack of a strong conditioning mechanism to guide the video generation based on the input text and audio.
D) Insufficient regularization in the generator network.
E) The input audio is too loud.

3. You are experimenting with different multimodal transformer architectures for a video understanding task. You are using a large pre- trained model and fine-tuning it on your specific dataset. You observe that the model is overfitting and struggling to generalize to unseen videos. Which of the following techniques would be most effective in mitigating overfitting in this scenario? (Choose two)

A) Implement weight decay and dropout regularization.
B) Reduce the number of transformer layers in the model.
C) Use a smaller pre-trained model.
D) Employ data augmentation techniques specifically designed for video data (e.g., temporal jittering, random cropping).
E) Increase the batch size significantly.

4. You are fine-tuning a pre-trained multimodal model for a specific task that involves generating short video clips from text prompts. The pre-trained model was trained on a large dataset of diverse videos and text descriptions. However, you observe that the fine-tuned model tends to generate video clips that are visually appealing but often deviate significantly from the meaning of the text prompts. Which of the following techniques is LEAST likely to improve the semantic consistency between the generated video clips and the text prompts?

A) Implement a reinforcement learning objective that rewards the model for generating videos that are semantically similar to the text prompts.
B) Freeze the weights of the video encoder during fine-tuning.
C) Augment the fine-tuning dataset with synthesized video-text pairs generated by another model.
D) Use a lower learning rate during fine-tuning.
E) Adding a contrastive loss to the fine-tuning process.

5. You are tasked with evaluating a text-to-video generation model. Which of the following metrics would be MOST appropriate for assessing the temporal coherence and smoothness of the generated videos?

A) Learned Perceptual Image Patch Similarity (LPIPS)
B) Inception Score (IS)
C) Frchet Inception Distance (FID)
D) Frchet Video Distance (FVD)
E) BLEU score

Solutions: