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NVIDIA Generative AI Multimodal Sample Questions:

1. You are working with a dataset containing text descriptions of products and corresponding product images. You want to train a model that can retrieve the most relevant image for a given text description. Which of the following loss functions is MOST appropriate for this task?

A) Binary Cross-entropy loss
B) Cross-entropy loss
C) Huber Loss
D) Triplet loss
E) Mean Squared Error (MSE) loss

2. You are evaluating a Generative A1 model for image captioning. Which of the following metrics is MOST appropriate for assessing the semantic similarity between the generated captions and the ground truth captions?

A) Inception Score
B) BLEU score
C) Perplexity
D) CIDEr score
E) ROUGE score

3. Consider the following Python code snippet using PyTorch, intended to combine image and text embeddings:

Which of the following statements regarding the output shapes of these combined embeddings are TRUE? (Select TWO)

A) combined_embedding_concat has shape (32, 1024).
B) combined_embedding_weighted has shape (32, 512).
C) combined_embedding_add has shape (32, 1024).
D) combined_embedding_weighted has shape (32, 1024).
E) combined_embedding_concat has shape (64, 512).

4. Which of the following losses are suitable and can be used when training a Variational Autoencoder (VAE)?

A) Hinge Loss
B) Cross-entropy loss
C) KL Divergence Loss
D) Mean Squared Error (MSE) loss
E) Contrastive Loss

5. You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?

A) Increase the weight decay (L2 regularization).
B) Implement early stopping based on the validation loss.
C) Increase the learning rate.
D) Decrease the learning rate.
E) Increase the training data size by collecting more data.

Solutions: