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NVIDIA Generative AI Multimodal Sample Questions:

1. You are building a multimodal application that takes an image and a short text description as input and generates a more detailed text description of the image. Which of the following model architectures is BEST suited for this task?

A) A Recurrent Neural Network (RNN) with attention mechanisms.
B) A Vision Transformer (ViT) for image encoding and a Transformer for text decoding.
C) A Multilayer Perceptron (MLP) for both image and text.
D) A simple CNN followed by an LSTM.
E) A Generative Adversarial Network (GAN) with separate image and text encoders.

2. Which of the following are key benefits of using multimodal learning compared to unimodal learning? (Select TWO correct answers)

A) Reduced computational complexity.
B) Improved robustness to noise and missing data in one modality.
C) Enhanced ability to capture complex relationships between different data types.
D) Simpler model architectures.
E) Guaranteed perfect accuracy.

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

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

5. Which of the following Python code snippets correctly demonstrates how to load pre-trained word embeddings (e.g., GloVe or Word2Vec) using spaCy and then calculate the cosine similarity between two words?

A)

B)

C)

D)

E)