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NVIDIA Generative AI Multimodal Sample Questions:

1. You are training a conditional generative model to generate images based on text descriptions. You notice that the generated images often lack fine-grained details and tend to be blurry, even though the overall structure matches the text description. Which of the following techniques would be MOST effective in improving the image quality and adding finer details?

A) Implement a perceptual loss function that compares high-level features of generated and real images.
B) Decrease the learning rate of the discriminator.
C) Use a simpler generator architecture.
D) Train the model for fewer epochs.
E) Increase the batch size used for training.

2. You are working with a dataset of handwritten digits and training a Variational Autoencoder (VAE) to generate new digits. After training, you observe that the generated digits are blurry and lack sharp details. Which of the following modifications could potentially improve the quality of the generated digits in your VAE?

A) Increasing the weight of the KL divergence term in the VAE loss function.
B) Increasing the capacity of the encoder and decoder networks (e.g., adding more layers or neurons).
C) Decreasing the dimensionality of the latent space.
D) Using a simpler decoder architecture.
E) Reducing the weight of the KL divergence term in the VAE loss function.

3. You are developing a multimodal generative A1 model that takes both image and text inputs. The image branch uses a ResNet50 pre- trained on ImageNet, while the text branch uses a BERT model. To effectively combine the features, you need to align their representations. Which of the following techniques is MOST suitable for projecting the image and text features into a common embedding space?

A) Using Principal Component Analysis (PCA) to reduce the dimensionality of ResNet50 and BERT features before concatenation.
B) Fine-tuning the entire ResNet50 and BERT models jointly on the multimodal dataset.
C) Direct concatenation of ResNet50 and BERT output features.
D) Training separate linear projection layers for both ResNet50 and BERT outputs, followed by concatenation.
E) Employing Contrastive Learning with a shared embedding space and using positive and negative pairs of image and text.

4. You are developing a multimodal AI model that processes both text and images to classify news articles as either 'reliable' or 'unreliable'. After training, you notice that the model performs well on articles with strong visual cues (e.g., professionally edited images), but struggles with articles that have only text or low-quality images. Which of the following techniques would be MOST effective in improving the model's robustness and generalizability across different types of news articles?

A) Increase the size of the training dataset by only adding more data with high quality images.
B) Replace the image processing component with a simpler, less powerful model.
C) Implement a modality dropout strategy during training, randomly masking either the text or image input to force the model to rely more on the available modality.
D) Exclusively train the model on articles with high-quality images to improve its visual processing capabilities.
E) Reduce the weight of the image modality in the overall loss function.

5. Consider a scenario where you are building a multimodal model to generate realistic indoor scenes. You have access to text descriptions of the scene, 3D models of furniture, and ambient sound recordings. Which of the following loss functions would be most appropriate to ensure coherence and realism in the generated scenes?

A) Cosine similarity loss between the generated image and the input 3D models.
B) A combination of adversarial loss (GAN) to ensure realism, a perceptual loss to match high-level features, and a semantic consistency loss to align the generated image with the input text description.
C) Mean Squared Error (MSE) between the generated image and a reference image.
D) KL Divergence loss between the generated sound and the input text.
E) Cross-entropy loss for classifying different object categories in the scene.

Solutions: