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NVIDIA Generative AI Multimodal Sample Questions:

1. You are deploying a multimodal Generative A1 model on a cloud platform. The model takes video and text as input to generate video descriptions. The model's performance needs to be monitored to ensure it meets certain performance SLAs. Which of the following metrics are MOST crucial to monitor in a production environment to ensure both computational efficiency and output quality? (Select TWO)

A) GPU utilization.
B) Inference latency (time per request).
C) BLEU score (or similar text generation metric) for generated descriptions.
D) Model size on disk.
E) Number of lines of code in the model.

2. You are building a system that uses text and images to generate 3D models. The text describes the object, and the images provide visual details. During training, you observe that the model heavily relies on the image input and largely ignores the text description. What technique can you employ to encourage the model to give more weight to the textual input?

A) Increase the size of the text vocabulary.
B) Increase the resolution of the input images.
C) Use a curriculum learning approach, starting with simpler text descriptions and gradually increasing the complexity.
D) Decrease the learning rate for the image processing branch of the model.
E) Apply a higher dropout rate to the image embedding layer.

3. When training a multimodal generative model for image captioning, you notice the model generates grammatically correct but generic and uninformative captions. Which technique is MOST likely to improve the in formativeness and specificity of the generated captions?

A) Decrese the size of the vocabulary.
B) Employ a diverse beam search or sampling strategy during inference to encourage exploration of different caption possibilities.
C) Use beam search during inference with a large beam size.
D) Decrease the learning rate during training.
E) Increase the size of the image encoder.

4. You are building a system that translates sign language videos into spoken text. You have a dataset of videos and corresponding text transcriptions. You notice that the test data contains significant variations in lighting conditions and camera angles compared to the training dat a. Which of the following techniques would be MOST effective in addressing this domain shift and improving the generalization of your model?

A) Only evaluate on a subset of the test data that closely resembles the training data.
B) Fine-tune the model on a small subset of the test data to adapt to the specific characteristics of the test distribution.
C) Use a domain adaptation technique such as Domain Adversarial Neural Networks (DANN) to learn domain-invariant features.
D) Reduce the size of the model to prevent overfitting to the training data.
E) Apply aggressive data augmentation techniques to the training data, including random crops, rotations, and color jittering to simulate the variations in the test data.

5. You are designing a IJ-Net architecture for semantic segmentation of medical images. Your input images are 512x512 with 3 channels.
You want to ensure the final output segmentation map is also 512x512. Which of the following design choices are crucial for achieving this resolution, considering the downsampling and upsampling stages?

A) Employing only IXI convolutions in the bottleneck of the U-Net architecture to reduce computational complexity.
B) Using only strided convolutions for downsampling and transposed convolutions for upsampling without skip connections.
C) Using a batch size of 1 during training to simplify memory management.
D) Using max pooling with a kernel size of 3x3 and stride of 2 for downsampling, and nearest neighbor interpolation for upsampling.
E) Ensuring that the number of downsampling and upsampling blocks are equal, and employing skip connections from corresponding encoder layers to decoder layers.

Solutions: