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NVIDIA Generative AI Multimodal Sample Questions:

1. Which of the following are valid techniques for dealing with overfitting in a deep learning model trained on image data?

A) Implementing dropout layers.
B) Reducing the amount of training data
C) Increasing the complexity of the model.
D) Using data augmentation techniques.
E) Adding Ll or L2 regularization.

2. Consider the following PyTorch code snippet for a GAN discriminator:

A) The code implements a non-saturating loss, designed to alleviate vanishing gradients in the discriminator.
B) The code will raise a 'ValueErroN' because 'torch.mean' expects a 'dim' argument.
C) The code will train without errors, but the discriminator's performance will be poor due to vanishing gradients.
D) The code will train without errors, but there is no significant impact on the discriminator.
E) The code implements a hinge loss, encouraging the discriminator to output values greater than 1 for real samples and less than -1 for fake samples.

3. You are tasked with deploying a generative A1 model for image inpainting using Triton Inference Server. The model requires significant GPU memory and you want to maximize throughput. Which Triton configuration parameters would be MOST important to tune, and why?

A) 'optimization' (setting strategy to TRT to enable TensorRT optimization) and 'input_shape' (specifying the exact input shape).
B) 'dynamic_batching' (enabling it and setting and 'model_warmup' (specifying dummy inputs to pre-load the model).
C) Both B and C.
D) 'instance_group' (setting count to the number of available GPUs) and (setting a high value to accumulate requests).
E) 'instance_group' (setting count to the number of available GPUs and kind to KIND_GPU) and (increasing it to the largest value that fits in GPU memory).

4. You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?

A) Increase the training data size by collecting more data.
B) Implement early stopping based on the validation loss.
C) Increase the weight decay (L2 regularization).
D) Decrease the learning rate.
E) Increase the learning rate.

5. You are tasked with optimizing a multimodal model that combines audio and text data for speech recognition. The model currently struggles with noisy audio environments. Which data augmentation technique would be MOST effective in improving the model's robustness to noise?

A) Translating the text into different languages and back.
B) Adding Gaussian noise to the audio data.
C) Normalizing the text data to lowercase.
D) Randomly masking parts of the text input.
E) Rotating the images used for visual context.

Solutions: