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NVIDIA Generative AI Multimodal Sample Questions:

1. Consider a multimodal generative model trained on a dataset of images and corresponding captions. After training, you observe that the model generates captions that are grammatically correct but often lack specific details and relevance to the input image. Which of the following regularization techniques is MOST likely to improve the faithfulness and informativeness of the generated captions?

A) Dropout during training.
B) Attention regularization to encourage the model to attend to relevant regions in the image when generating the caption.
C) L1 regularization on the model weights.
D) Adding Gaussian noise to the input images.
E) KL divergence regularization to encourage the generated caption distribution to be similar to the prior caption distribution.

2. You are building a system to generate captions for images. You want to evaluate how well the generated captions describe the content of the images. Which of the following metrics are most suitable for evaluating the quality of image captions?

A) ROUGE (Recall-Oriented Understudy for Gisting Evaluation).
B) Inception Score.
C) F 1-Score
D) BLEU (Bilingual Evaluation Understudy).
E) Pixel Accuracy.

3. You're building a system that takes a medical image (e.g., X-ray) and a patient's medical history (text) as input, predicting the likelihood of a specific disease. You want to use SHAP (SHapley Additive exPlanations) values to explain the model's predictions. How would you adapt SHAP to handle both image and text inputs effectively?

A) Apply KernelSHAP separately to the image and text, then combine the results.
B) Use a multimodal SHAP implementation that is designed to handle both image and text features simultaneously, considering their interaction.
C) Use DeepExplainer for the image component and a simple linear SHAP explainer for the text.
D) Represent both the image and text as numerical vectors and then apply a standard SHAP explainer.
E) Treat the image and text as separate models and explain each independently.

4. You are training a multimodal model that combines text and images. You observe that the model is heavily biased towards the text modality and largely ignores the image data. Which of the following strategies could you use to address this modality imbalance? (Select all that apply)

A) Decrease the learning rate for the text-related parameters of the model.
B) Use a modality-specific loss weighting scheme, assigning a higher weight to the loss component derived from the image data.
C) Oversample the image data during training.
D) Increase the learning rate for the image-related parameters of the model.
E) Reduce the dimensionality of the image features to match the dimensionality of the text embeddings.

5. You are deploying a multimodal model that uses both video and audio data for real-time emotion recognition. The model is deployed on an edge device with limited computational resources. Which optimization techniques would be MOST effective for reducing latency and improving the model's inference speed on the edge device?

A) Using full precision (FP32) for all model operations.
B) Increasing the resolution of the video input.
C) Transmitting the video and audio data to a cloud server for inference.
D) Increasing the model's complexity to improve accuracy.
E) Quantizing the model to a lower precision (e.g., INT8) and pruning less important connections.

Solutions: