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NVIDIA Generative AI Multimodal Sample Questions:

1. Which data augmentation techniques are MOST suitable for improving the robustness of a multimodal model that uses images and text?

A) Rotating images and back-translating text (translating to another language and back to the original).
B) Changing the image resolution and increasing the text size.
C) Adding Gaussian noise to images and randomly deleting words in the text.
D) Randomly cropping images and translating text to different languages.
E) Rescaling images and changing the font of the text.

2. You are building a multi-modal model that combines text and image data for a search application. The goal is to retrieve relevant images given a text query. You have encoded both images and text into embeddings. What's a suitable loss function for training the model to ensure images relevant to a text query are ranked higher than irrelevant ones?

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

3. You observe that the generated images often lack fine-grained details and tend to be blurry. Which of the following techniques could MOST effectively improve the visual quality of the generated images?

A) Using a larger dataset of text-image pairs.
B) Implementing a discriminator network and using adversarial training (GAN).
C) Increasing the batch size during training.
D) Using a variational autoencoder (VAE) instead of a GAN.unlikely to significantly improve diagnosis accuracy.
E) Decreasing the learning rate during training.

4. You have trained a multimodal model to generate descriptions for recipes, using images of the finished dish as one modality and a list of ingredients as another. When evaluating the generated descriptions, you notice the descriptions are factually correct in terms of ingredients but often fail to capture the stylistic nuances or tone of professionally written recipes. Which evaluation strategy would provide the most insightful feedback on this aspect of the model's performance?

A) Compute the ROUGE score between the generated descriptions and a set of reference recipes.
B) Use a pre-trained language model to assess the fluency and coherence of the generated descriptions.
C) Conduct a human evaluation where individuals rate the generated descriptions based on factors such as informativeness, clarity, and stylistic appropriateness.
D) Measure the perplexity of the generated descriptions using a language model trained on a corpus of recipe books.
E) Calculate the BLEU score between the generated descriptions and a set of reference recipes.

5. You're building a virtual assistant using NVIDIAAvatar Cloud Engine (ACE). You want the avatar to respond to user queries with realistic facial expressions and lip synchronization. Which ACE components are essential for achieving this?

A) Riva ASR, Riva TTS, and Audi02Emotion.
B) Riva ASR, Riva TTS, Audi02Emotion, a 3D avatar model, and an animation engine.
C) Riva ASR, Riva TTS, Audi02Emotion, and a 3D avatar model.
D) only Riva ASR and TTS.
E) Only a 3D avatar model.

Solutions: