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NVIDIA Generative AI Multimodal Sample Questions:

1. You are working on a multimodal model for autonomous driving that uses lidar, camera, and radar dat a. During testing, you notice that the model performs poorly in adverse weather conditions (e.g., heavy rain, fog). Which of the following strategies could you implement to improve the model's robustness to these conditions?

A) Increase the learning rate during training when adverse weather data is present.
B) Train separate models for different weather conditions and switch between them based on weather sensor readings.
C) Reduce the model complexity to prevent overfitting to specific weather conditions.
D) Use domain adaptation techniques to bridge the gap between simulated and real-world data in adverse weather.
E) Augment the training data with synthetically generated data representing adverse weather conditions.

2. Which of the following techniques are MOST relevant to optimizing the energy efficiency of a large multimodal generative A1 model deployed on NVIDIA GPUs? (Select TWO)

A) Implementing model parallelism across multiple GPUs without optimizing communication overhead.
B) Adding more data augmentation techniques to the training process.
C) Increasing the size of the hidden layers in the transformer architecture.
D) Knowledge distillation, transferring the knowledge to a smaller model.
E) Using mixed precision training (e.g., FP16) to reduce memory usage and computation.

3. You are building a system that generates image captions from images and vice vers a. Which evaluation metric(s) are MOST appropriate to assess the quality of the generated content? (Select all that apply)

A) FID (Frechet Inception Distance)
B) Inception Score
C) Accuracy
D) BLEU score
E) ROUGE score

4. During data analysis for a multimodal A1 project involving image and text data, you discover that the image dataset contains a large number of blurry or low-resolution images. The text data, however, is relatively clean and well-structured. What is the BEST approach to mitigate the impact of the noisy image data on the overall model performance?

A) Train the model on the noisy image data without any preprocessing or data augmentation.
B) Increase the weight of the text data during model training to compensate for the noisy image data.
C) Apply image enhancement techniques such as sharpening and super-resolution to improve the quality of the blurry images.
D) Discard the blurry and low-resolution images from the dataset to ensure data quality.
E) Use a combination of image enhancement techniques and robust loss functions that are less sensitive to noisy data.

5. You're developing a real-time video captioning system. Latency is critical. Which of the following optimization strategies would provide the MOST significant reduction in end-to-end latency, assuming the captioning model is already optimized for inference?

A) All of the above.
B) Caching the most frequent words in a local vocabulary.
C) Overlapping image feature extraction and caption generation using asynchronous execution.
D) Using a smaller batch size for inference.
E) Employing model quantization (e.g., converting weights from FP32 to INT8).

Solutions: