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NVIDIA Generative AI Multimodal Sample Questions:

1. You are developing a generative A1 model for medical image segmentation using U-Net architecture. The input images are high- resolution MRI scans. Which of the following techniques would be MOST effective in mitigating the vanishing gradient problem during training, considering memory constraints on your GPU?

A) Replacing standard convolutional layers with transposed convolutional layers throughout the network.
B) Implementing skip connections within the U-Net architecture and using ReLU activation functions.
C) Using a smaller batch size and increasing the number of training epochs.
D) Increasing the depth of the U-Net and using sigmoid activation functions.
E) Employing gradient clipping and using Leaky ReLU or ELU activation functions.

2. You are training a conditional generative model to generate images based on text descriptions. You notice that the generated images often lack fine-grained details and tend to be blurry, even though the overall structure matches the text description. Which of the following techniques would be MOST effective in improving the image quality and adding finer details?

A) Decrease the learning rate of the discriminator.
B) Use a simpler generator architecture.
C) Train the model for fewer epochs.
D) Implement a perceptual loss function that compares high-level features of generated and real images.
E) Increase the batch size used for training.

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

4. You are developing an Avatar Cloud Engine (ACE) application for a virtual assistant that needs to generate realistic facial expressions based on user emotions detected from text. Which ACE microservice would be most directly responsible for this functionality?

A) Facial Animation
B) Text to Speech (TTS)
C) Natural Language Understanding (NLU)
D) Lip Sync
E) speech to Text (STT)

5. Which of the following evaluation metrics is MOST appropriate for assessing the performance of a multimodal generative A1 model that generates image captions based on images and audio descriptions?

A) Root Mean Squared Error (RMSE)
B) Perplexity
C) Inception Score
D) Mean Squared Error (MSE)
E) BLEU (Bilingual Evaluation Understudy)

Solutions: