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NVIDIA Generative AI Multimodal Sample Questions:

1. You are analyzing the performance of a Generative A1 model and notice that it is overfitting to the training dat a. Which techniques can you apply to mitigate overfitting and improve the model's generalization performance? Select all that apply:

A) Increase the learning rate.
B) Use dropout layers during training.
C) Decrease the model's complexity (e.g., reduce the number of layers or parameters).
D) Increase the size of the training dataset.
E) Add L1 or L2 regularization to the model's loss function.

2. You're training a model to generate code snippets from natural language descriptions. You are using a Transformer architecture and a large dataset of code examples. You notice the model frequently generates syntactically correct code, but the code doesn't accurately implement the described functionality (i.e., it's semantically incorrect). Select TWO methods which could improve the semantic correctness of the generated code.

A) Fine-tune the model on a subset of the data where each code snippet is accompanied by unit tests, and train the model to also generate these tests.
B) Increase the size of the vocabulary used by the model.
C) Use a reinforcement learning approach where the reward is based on whether the generated code passes the unit tests associated with the descriptiom
D) Increase the number of Transformer layers.
E) Apply techniques like beam search during decoding to generate more diverse code snippets.

3. Consider this PyTorch code snippet related to processing multimodal dat a. What is the primary purpose of the following code in the context of Generative A1?

A) To resize all images to the same dimension.
B) To create separate data loaders for images and text.
C) To ensure images and text are processed in the same order during training.
D) To create a custom dataset class for handling paired image and text data.
E) To concatenate image and text data into a single tensor.

4. A self-driving car uses multimodal data (camera images, LiDAR point clouds, radar data, and GPS information) to navigate. The LiDAR sensor occasionally fails, resulting in missing point cloud dat a. How should the system be designed to handle this sensor failure gracefully and maintain safe navigation?

A) Rely solely on the camera images and ignore the missing LiDAR data.
B) Use a sensor fusion technique that prioritizes the available modalities (camera, radar, GPS) and estimates the missing LiDAR data based on these modalities.
C) Immediately stop the car until the LiDAR sensor is fixed.
D) Employ a Kalman filter to predict the LiDAR point cloud based on the previous sensor readings and the car's motion model.
E) Switch to a pre-programmed route that does not require LiDAR data.

5. You are tasked with building a Generative A1 model that can generate realistic images of birds based on text descriptions. You have a large dataset of bird images and corresponding text captions. Which of the following architectures is MOST suitable for this task, considering both image quality and training efficiency?

A) A standard Convolutional Neural Network (CNN) for image generation.
B) A simple Recurrent Neural Network (RNN) to generate pixel values sequentially.
C) A Variational Autoencoder (VAE) trained on the image dataset.
D) A Generative Adversarial Network (GAN) conditioned on the text descriptions (e.g., a StackGAN or AttnGAN).
E) An Image Transformer model trained from scratch.

Solutions: