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NVIDIA Generative AI Multimodal Sample Questions:

1. Consider the following Python code snippet using PyTorch. What does this code do in the context of data preprocessing for a Generative AI model?

A)

B)

C)

D)

E)

2. Consider a scenario where you are using a pre-trained multimodal model for image captioning and want to fine-tune it on a specific dataset. Which of the following strategies is MOST likely to lead to improved performance and faster convergence?

A) Randomly initialize the entire model and train from scratch.
B) Fine-tune the entire model (image encoder and captioning head) with a very large learning rate.
C) Fine-tune only the captioning head (language model) while keeping the image encoder frozen.
D) Train a new captioning head from scratch while keeping the image encoder frozen.
E) Fine-tune the entire model with a smaller learning rate and gradually unfreeze layers, starting from the captioning head.

3. A multimodal A1 model is trained on a dataset containing biased text and images. This bias leads to the model generating outputs that reinforce negative stereotypes. Which of the following steps are crucial for addressing and mitigating this bias during the model development lifecycle? (Select TWO)

A) Reducing the number of layers in the neural network.
B) Increasing the learning rate during training.
C) Collecting a more diverse and representative dataset.
D) Using adversarial training techniques to encourage fairness.
E) Implementing model distillation to reduce the model size

4. In the context of multimodal data analysis, which of the following statements accurately describe the challenges associated with data alignment?

A) Data alignment is only necessary when dealing with time-series data.
B) Misalignment can lead to spurious correlations and reduced model performance.
C) Data alignment ensures that data from different modalities refers to the same event or entity.
D) Data alignment is not relevant when using deep learning models.
E) Perfect data alignment is always achievable with proper preprocessing techniques.

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

Solutions: