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NVIDIA Generative AI Multimodal Sample Questions:

1. Which of the following techniques is MOST suitable for aligning the feature spaces of text and images in a multimodal model?

A) Training the text and image encoders independently.
B) Using separate loss functions for text and image encoders.
C) Concatenating the features from the text and image encoders without any further processing.
D) Employing a contrastive loss function that encourages similar representations for semantically related text and images.
E) Only using image data during the training process.

2. You are building a system that translates sign language videos into spoken text. You have a dataset of videos and corresponding text transcriptions. You notice that the test data contains significant variations in lighting conditions and camera angles compared to the training dat a. Which of the following techniques would be MOST effective in addressing this domain shift and improving the generalization of your model?

A) Reduce the size of the model to prevent overfitting to the training data.
B) Fine-tune the model on a small subset of the test data to adapt to the specific characteristics of the test distribution.
C) Apply aggressive data augmentation techniques to the training data, including random crops, rotations, and color jittering to simulate the variations in the test data.
D) Use a domain adaptation technique such as Domain Adversarial Neural Networks (DANN) to learn domain-invariant features.
E) Only evaluate on a subset of the test data that closely resembles the training data.

3. Consider a scenario where you are developing a multimodal system for generating 3D models from text descriptions. The system uses a Variational Autoencoder (VAE) to generate the 3D models. During training, you observe that the generated 3D models lack diversity and tend to cluster around a few common shapes. Which of the following techniques could you employ to improve the diversity of the generated 3D models?

A) Using a larger training dataset with more diverse text descriptions.
B) Increasing the weight of the Kullback-Leibler (KL) divergence term in the VAE's loss function.
C) Decreasing the batch size during training.
D) Decreasing the capacity of the VAE's latent space.
E) Applying techniques like adversarial training to encourage the VAE to generate more realistic 3D models.

4. You are working with a large dataset of images for training a generative model. The dataset contains a significant amount of noise and outliers. Which of the following data preprocessing techniques would be MOST effective in mitigating the impact of noise and outliers on the model's performance?

A) Converting all images to grayscale.
B) Clipping pixel values to a specific range (e.g., [0, 255]).
C) Applying histogram equalization to all images.
D) Using a robust statistics-based normalization technique (e.g., Z-score normalization with median and interquartile range).
E) Applying a Gaussian blur to all images.

5. You are building a multimodal AI system that generates 3D models of furniture from text descriptions and a few 2D images of similar furniture pieces. The system uses separate encoders for text and images. You want to fuse the information from both modalities effectively. Which TWO of the following fusion techniques would be the most appropriate for this task, considering the different nature of the text and image data?

A) Using a cross-attention mechanism where the text embedding attends to the image features, and vice-versa, allowing the model to dynamically weight the importance of different parts of each modality.
B) Using a gating mechanism (e.g., a learned weight) to control the contribution of the text and image embeddings based on the input.
C) Simple concatenation of the text and image embeddings before feeding them into the decoder.
D) Performing a simple average of the text and image embeddings.
E) Applying element-wise addition of the text and image embeddings.

Solutions: