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NVIDIA Generative AI Multimodal Sample Questions:

1. You are building a multimodal application that takes an image and a text prompt as input to generate a modified image. The image is of a cat, and the text prompt is 'wearing a hat'. Which of the following models or techniques would be MOST suitable for achieving this task efficiently and effectively?

A) A diffusion model conditioned on both image and text embeddings, such as Stable Diffusion or DALL-E 2.
B) A recurrent neural network (RNN) for sequence modeling.
C) A large language model (LLM) fine-tuned solely on text data.
D) A simple convolutional neural network (CNN) for image classification.
E) A Vision Transformer (ViT) trained only on image classification tasks.

2. You are analyzing the latent space of a GAN trained to generate images of human faces. You notice that interpolating between two points in the latent space often results in unrealistic or distorted faces. Which of the following techniques could potentially improve the smoothness and interpretability of the latent space?

A) Decreasing the learning rate of the generator network.
B) Using spectral normalization in the discriminator network.
C) Using a smaller batch size during training.
D) Increasing the number of layers in the discriminator network.
E) Applying a regularization term to the latent space during training to encourage smoothness (e.g., a Laplacian prior).

3. 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)

4. You are tasked with evaluating the trustworthiness of a multimodal A1 model that predicts diagnoses based on medical images and patient history text. Which of the following evaluation metrics or techniques are MOST relevant for assessing the model's trustworthiness in this critical application?

A) Attribution methods (e.g., Grad-CAM) to visualize which parts of the image and text the model focuses on.
B) Calibration error, measuring the alignment between predicted probabilities and actual outcomes.
C) Measuring inference throughput (samples per second).
D) Robustness testing by introducing adversarial perturbations to the input data.
E) Accuracy and F1-score on a held-out test set.

5. You are developing a multimodal system for medical diagnosis using MRI images and patient history text. Your initial model performs poorly on patients with rare conditions. Which of the following data augmentation techniques would be MOST effective in improving the model's performance on these under-represented cases?

A) Adding Gaussian noise to the pixel values of MRI images.
B) Randomly cropping and rotating MRI images.
C) Replacing named entities in the patient history text with other random entities-
D) Back-translating the patient history text to generate paraphrased versions-
E) Using a Generative Adversarial Network (GAN) to synthesize new MRI images conditioned on the patient history text, specifically targeting the rare conditions-

Solutions: